BACKGROUND OF THE INVENTION [0002] This invention relates generally to analysis of the activity of a chemical reaction network and, more specifically, to computational methods for simulating and predicting the activity of Saccharomyces cerevisiae (S. cerevisiae) reaction networks.

[0003] Saccharomyces cerevisiae is one of the best-studied microorganisms and in addition to its significant industrial importance it serves as a model organism for the study of eukaryotic cells (Winzeler et al. Science 285: 901-906 (1999) ). Up to 30% of positionally cloned genes implicated in human disease have yeast homologs.

[0004] The first eukaryotic genome to be sequenced was that of S. cerevisiae, and about 6400 open reading frames (or genes) have been identified in the genome. S. cerevisiae was the subject of the first expression profiling experiments and a compendium of expression profiles for many different mutants and different growth conditions has been established.

Furthermore, a protein-protein interaction network has been defined and used to study the interactions between a large number of yeast proteins.

[0005] S. cerevisiae is used industrially to produce fuel ethanol, technical ethanol, beer, wine, spirits and baker's yeast, and is used as a host for production of many pharmaceutical proteins (hormones and vaccines). Furthermore, S. cerevisiae is currently being exploited as a cell factory for many different bioproducts including insulin.

[0006] Genetic manipulations, as well as changes in various fermentation conditions, are being considered in an attempt to improve the yield of industrially important products made by S. cerevisiae. However, these approaches are currently not guided by a clear understanding of how a change in a particular parameter, or combination of parameters, is likely to affect cellular behavior, such as the growth of the organism, the production of the desired product or the production of unwanted by-products. It would be valuable to be able to predict how changes in fermentation conditions, such as an increase or decrease in the supply of oxygen or a media component, would affect cellular behavior and, therefore, fermentation performance. Likewise, before engineering the organism by addition or deletion of one or more genes, it would be useful to be able to predict how these changes would affect cellular behavior.

[0007] However, it is currently difficult to make these sorts of predictions for S. cerevisiae because of the complexity of the metabolic reaction network that is encoded by the S. cerevisiae genome. Even relatively minor changes in media composition can affect hundreds of components of this network such that potentially hundreds of variables are worthy of consideration in making a prediction of fermentation behavior. Similarly, due to the complexity of interactions in the network, mutation of even a single gene can have effects on multiple components of the network. Thus, there exists a need for a model that describes S. cerevisiae reaction networks, such as its metabolic network, which can be used to simulate many different aspects of the cellular behavior of S. cerevisiae under different conditions.

The present invention satisfies this need, and provides related advantages as well.

SUMMARY OF THE INVENTION [0008] The invention provides a computer readable medium or media, including: (a) a data structure relating a plurality of reactants in S. cerevisiae to a plurality of reactions in S. cerevisiae, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, (b) a constraint set for the plurality of S. cerevisiae reactions, and (c) commands for determining at least one flux distribution that minimizes or maximizes an objective function when the constraint set is applied to the data representation, wherein at least one flux distribution is predictive of a physiological function of S. cerevisiae. In one embodiment, at least one of the cellular reactions in the data structure is annotated to indicate an associated gene and the computer readable medium or media further includes a gene database including information characterizing the associated gene. In another embodiment, at least one of the cellular reactions in the data structure is annotated with an assignment of function within a subsystem or a compartment within the cell.

10009] The invention also provides a method for predicting physiological function of S. cerevisiae, including: (a) providing a data structure relating a plurality of S. cerevisiae to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function. In one embodiment, at least one of the S. cerevisiae reactions in the data structure is annotated to indicate an associated gene and the method predicts a S. cerevisiae physiological function related to the gene.

[0010] Also provided by the invention is a method for making a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions in a computer readable medium or media, including: (a) identifying a plurality of S. cerevisiae reactions and a plurality of reactants that are substrates and products of the reactions; (b) relating the plurality of reactants to the plurality of reactions in a data structure, wherein each of the reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (c) determining a constraint set for the plurality of S. cerevisiae reactions; (d) providing an objective function; (e) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, and (f) if at least one flux distribution is not predictive of a physiological function of S. cerevisiae, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if at least one flux distribution is predictive of a physiological function of the eukaryotic cell, then storing the data structure in a computer readable medium or media. The invention further provides a data structure relating a plurality of S. cerevisiae reactants to a plurality of reactions, wherein the data structure is produced by the method.

BRIEF DESCRIPTION OF THE DRAWINGS [0011] Figure 1 shows a schematic representation of a hypothetical metabolic network.

[0012] Figure 2 shows the stoichiometric matrix (S) for the hypothetical metabolic network shown in Figure 1.

[0013] Figure 3 shows mass balance constraints and flux constraints (reversibility constraints) that can be placed on the hypothetical metabolic network shown in Figure 1. (oo, infinity; Yl, uptake rate value) [0014] Figure 4 shows an exemplary metabolic reaction network in S. cerevisiae.

[0015] Figure 5 shows a method for reconstruction of the metabolic network of S. cerevisiae. Based on the available information from the genome annotation, biochemical pathway databases, biochemistry textbooks and recent publications, a genome-scale metabolic network for S. cerevisiae was designed. Additional physiological constraints were considered and modeled, such as growth, non-growth dependent ATP requirements and biomass composition.

[0016] Figure 6 shows a Phenotypic Phase Plane (PhPP) diagram for S. cerevisiae revealing a finite number of qualitatively distinct patterns of metabolic pathway utilization divided into discrete phases. The characteristics of these distinct phases are interpreted using ratios of shadow prices in the form of isoclines. The isoclines can be used to classify these phases into futile, single and dual substrate limitation and to define the line of optimality.

The upper part of the figure shows a 3-dimensional S. cerevisiae Phase Plane diagram. The bottom part shows a 2-dimensional Phase Plane diagram with the line of optimality (LO) indicated.

[0017] Figure 7 shows the respiratory quotient (RQ) versus oxygen uptake rate (mmole/g- DW/hr) (upper left) on the line of optimality. The phenotypic phase plane (PhPP) illustrates that the predicted RQ is a constant of value 1.06 [0018] Figure 8 shows phases of metabolic phenotype associated with varying oxygen availability, from completely anaerobic fermentation to aerobic growth in S. cerevisiae. The glucose uptake rate was fixed under all conditions, and the resulting optimal biomass yield, as well as respiratory quotient, RQ, are indicated along with the output fluxes associated with four metabolic by-products: acetate, succinate, pyruvate, and ethanol.

[0019] Figure 9 shows anaerobic glucose limited continuous culture of S. cerevisiae.

Figure 9 shows the utilization of glucose at varying dilution rates in anaerobic chemostat culture. The data-point at the dilution rate of 0.0 is extrapolated from the experimental results. The shaded area or the infeasible region contains a set of stoichiometric constraints that cannot be balanced simultaneously with growth demands. The model produces the optimal glucose uptake rate for a given growth rate on the line of optimal solution (indicated by Model (optimal) ). Imposition of additional constraints drives the solution towards a region where more glucose is needed (i. e. region of alternative sub-optimal solution). At the optimal solution, the in silico model does not secrete pyruvate and acetate. The maximum difference between the model and the experimental points is 8% at the highest dilution rate.

When the model is forced to produce these by-products at the experimental level (Model (forced) ), the glucose uptake rate is increased and becomes closer to the experimental values.

Figure 9B and 9C show the secretion rate of anaerobic by-products in chemostat culture. (q, secretion rate; D, dilution rate).

[0020] Figure 10 shows aerobic glucose-limited continuous culture of S. cerevisiae in vivo and in silico. Figure l0A shows biomass yield (Yx), and secretion rates of ethanol (Eth), and glycerol (Gly). Figure lOB shows CO2 secretion rate (qc02) and respiratory quotient (RQ; i. e. qc02/qO2) of the aerobic glucose-limited continuous culture of S. cerevisiae. (exp, experimental).

DETAILED DESCRIPTION OF THE INVENTION [0021] The present invention provides an in silico model of the baker's and brewer's yeast, S. cerevisiae, that describes the interconnections between the metabolic genes in the S. cerevisiae genome and their associated reactions and reactants. The model can be used to simulate different aspects of the cellular behavior of S. cerevisiae under different environmental and genetic conditions, thereby providing valuable information for industrial and research applications. An advantage of the model of the invention is that it provides a holistic approach to simulating and predicting the metabolic activity of S. cerevisiae.

[0022] As an example, the S. cerevisiae metabolic model can be used to determine the optimal conditions for fermentation performance, such as for maximizing the yield of a specific industrially important enzyme. The model can also be used to calculate the range of cellular behaviors that S. cerevisiae can display as a function of variations in the activity of one gene or multiple genes. Thus, the model can be used to guide the organismal genetic makeup for a desired application. This ability to make predictions regarding cellular behavior as a consequence of altering specific parameters will increase the speed and efficiency of industrial development of S. cerevisiae strains and conditions for their use.

[0023] The S. cerevisiae metabolic model can also be used to predict or validate the assignment of particular biochemical reactions to the enzyme-encoding genes found in the genome, and to identify the presence of reactions or pathways not indicated by current genomic data. Thus, the model can be used to guide the research and discovery process, potentially leading to the identification of new enzymes, medicines or metabolites of commercial importance.

[0024] The models of the invention are based on a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.

[0025] As used herein, the term"S. cerevisiae reaction"is intended to mean a conversion that consumes a substrate or forms a product that occurs in or by a viable strain of S. cerevisiae. The term can include a conversion that occurs due to the activity of one or more enzymes that are genetically encoded by a S. cerevisiae genome. The term can also include a conversion that occurs spontaneously in a S. cerevisiae cell. Conversions included in the term include, for example, changes in chemical composition such as those due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, glycolysation, reduction, oxidation or changes in location such as those that occur due to a transport reaction that moves a reactant within the same compartment or from one cellular compartment to another. In the case of a transport reaction, the substrate and product of the reaction can be chemically the same and the substrate and product can be differentiated according to location in a particular cellular compartment. Thus, a reaction that transports a chemically unchanged reactant from a first compartment to a second compartment has as its substrate the reactant in the first compartment and as its product the reactant in the second compartment. It will be understood that when used in reference to an in silico model or data structure, a reaction is intended to be a representation of a chemical conversion that consumes a substrate or produces a product.

[0026] As used herein, the term"S. cerevisiae reactant"is intended to mean a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S. cerevisiae. The term can include substrates or products of reactions performed by one or more enzymes encoded by S. cerevisiae gene (s), reactions occurring in S. cerevisiae that are performed by one or more non-genetically encoded macromolecule, protein or enzyme, or reactions that occur spontaneously in a S. cerevisiae cell. Metabolites are understood to be reactants within the meaning of the term. It will be understood that when used in reference to an in silico model or data structure, a reactant is intended to be a representation of a chemical that is a substrate or a product of a reaction that occurs in or by a viable strain of S. cerevisiae.

[0027] As used herein the term"substrate"is intended to mean a reactant that can be converted to one or more products by a reaction. The term can include, for example, a reactant that is to be chemically changed due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, reduction, oxidation or that is to change location such as by being transported across a membrane or to a different compartment.

[0028] As used herein, the term"product"is intended to mean a reactant that results from a reaction with one or more substrates. The term can include, for example, a reactant that has been chemically changed due to nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deamination, phosphorylation, methylation, reduction or oxidation or that has changed location such as by being transported across a membrane or to a different compartment.

[0029] As used herein, the term"stoichiometric coefficient"is intended to mean a numerical constant correlating the number of one or more reactants and the number of one or more products in a chemical reaction. Typically, the numbers are integers as they denote the number of molecules of each reactant in an elementally balanced chemical equation that describes the corresponding conversion. However, in some cases the numbers can take on non-integer values, for example, when used in a lumped reaction or to reflect empirical data.

[0030] As used herein, the term"plurality, "when used in reference to S. cerevisiae reactions or reactants is intended to mean at least 2 reactions or reactants. The term can include any number of S. cerevisiae reactions or reactants in the range from 2 to the number of naturally occurring reactants or reactions for a particular strain of S. cerevisiae. Thus, the term can include, for example, at least 10, 20,30, 50, 100, 150, 200,300, 400,500, 600 or more reactions or reactants. The number of reactions or reactants can be expressed as a portion of the total number of naturally occurring reactions for a particular strain of S. cerevisiae such as at least 20%, 30%, 50%, 60%, 75%, 90%, 95% or 98% of the total number of naturally occurring reactions that occur in a particular strain of S. cerevisiae.

[0031] As used herein, the term"data structure"is intended to mean a physical or logical relationship among data elements, designed to support specific data manipulation functions.

The term can include, for example, a list of data elements that can be added combined or otherwise manipulated such as a list of representations for reactions from which reactants can be related in a matrix or network. The term can also include a matrix that correlates data elements from two or more lists of information such as a matrix that correlates reactants to reactions. Information included in the term can represent, for example, a substrate or product of a chemical reaction, a chemical reaction relating one or more substrates to one or more products, a constraint placed on a reaction, or a stoichiometric coefficient.

[0032] As used herein, the term"constraint"is intended to mean an upper or lower boundary for a reaction. A boundary can specify a minimum or maximum flow of mass, electrons or energy through a reaction. A boundary can further specify directionality of a reaction. A boundary can be a constant value such as zero, infinity, or a numerical value such as an integer and non-integer.

[0033] As used herein, the term"activity,"when used in reference to a reaction, is intended to mean the rate at which a product is produced or a substrate is consumed. The rate at which a product is produced or a substrate is consumed can also be referred to as the flux for the reaction.

[0034] As used herein, the term"activity, "when used in reference to S. cerevisiae is intended to mean the rate of a change from an initial state of S. cerevisiae to a final state of S. cerevisiae. The term can include, the rate at which a chemical is consumed or produced by S. cerevisiae, the rate of growth of S. cerevisiae or the rate at which energy or mass flow through a particular subset of reactions.

[0035] The invention provides a computer readable medium, having a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product.

[0036] The plurality of S. cerevisiae reactions can include reactions of a peripheral metabolic pathway. As used herein, the term"peripheral, "when used in reference to a metabolic pathway, is intended to mean a metabolic pathway that includes one or more reactions that are not a part of a central metabolic pathway. As used herein, the term "central, "when used in reference to a metabolic pathway, is intended to mean a metabolic pathway selected from glycolysis, the pentose phosphate pathway (PPP), the tricarboxylic acid (TCA) cycle and the electron transfer system (ETS), associated anapleurotic reactions, and pyruvate metabolism.

[0037] A plurality of S. cerevisiae reactants can be related to a plurality of S. cerevisiae reactions in any data structure that represents, for each reactant, the reactions by which it is consumed or produced. Thus, the data structure, which is referred to herein as a"reaction network data structure, "serves as a representation of a biological reaction network or system.

An example of a reaction network that can be represented in a reaction network data structure of the invention is the collection of reactions that constitute the metabolic reactions of S. cerevisiae.

[0038] The methods and models of the invention can be applied to any strain of S. cerevisiae including, for example, strain CEN. PK113. 7D or any laboratory or production strain. A strain of S. cerevisiae can be identified according to classification criteria known in the art. Classification criteria include, for example, classical microbiological characteristics, such as those upon which taxonomic classification is traditionally based, or evolutionary distance as determined for example by comparing sequences from within the genomes of organisms, such as ribosome sequences.

[0039] The reactants to be used in a reaction network data structure of the invention can be obtained from or stored in a compound database. As used herein, the term"compound database"is intended to mean a computer readable medium or media containing a plurality of molecules that includes substrates and products of biological reactions. The plurality of molecules can include molecules found in multiple organisms, thereby constituting a universal compound database. Alternatively, the plurality of molecules can be limited to those that occur in a particular organism, thereby constituting an organism-specific compound database. Each reactant in a compound database can be identified according to the chemical species and the cellular compartment in which it is present. Thus, for example, a distinction can be made between glucose in the extracellular compartment versus glucose in the cytosol. Additionally each of the reactants can be specified as a metabolite of a primary or secondary metabolic pathway. Although identification of a reactant as a metabolite of a primary or secondary metabolic pathway does not indicate any chemical distinction between the reactants in a reaction, such a designation can assist in visual representations of large networks of reactions.

[0040] As used herein, the term"compartment"is intended to mean a subdivided region containing at least one reactant, such that the reactant is separated from at least one other reactant in a second region. A subdivided region included in the term can be correlated with a subdivided region of a cell. Thus, a subdivided region included in the term can be, for example, the intracellular space of a cell; the extracellular space around a cell; the periplasmic space; the interior space of an organelle such as a mitochondrium, endoplasmic reticulum, Golgi apparatus, vacuole or nucleus; or any subcellular space that is separated from another by a membrane or other physical barrier. Subdivided regions can also be made in order to create virtual boundaries in a reaction network that are not correlated with physical barriers. Virtual boundaries can be made for the purpose of segmenting the reactions in a network into different compartments or substructures.

[0041] As used herein, the term"substructure"is intended to mean a portion of the information in a data structure that is separated from other information in the data structure such that the portion of information can be separately manipulated or analyzed. The term can include portions subdivided according to a biological function including, for example, information relevant to a particular metabolic pathway such as an internal flux pathway, exchange flux pathway, central metabolic pathway, peripheral metabolic pathway, or secondary metabolic pathway. The term can include portions subdivided according to computational or mathematical principles that allow for a particular type of analysis or manipulation of the data structure.

[0042] The reactions included in a reaction network data structure can be obtained from a metabolic reaction database that includes the substrates, products, and stoichiometry of a plurality of metabolic reactions of S. cerevisiae. The reactants in a reaction network data structure can be designated as either substrates or products of a particular reaction, each with a stoichiometric coefficient assigned to it to describe the chemical conversion taking place in the reaction. Each reaction is also described as occurring in either a reversible or irreversible direction. Reversible reactions can either be represented as one reaction that operates in both the forward and reverse direction or be decomposed into two irreversible reactions, one corresponding to the forward reaction and the other corresponding to the backward reaction.

[0043] Reactions included in a reaction network data structure can include intra-system or exchange reactions. Intra-system reactions are the chemically and electrically balanced interconversions of chemical species and transport processes, which serve to replenish or drain the relative amounts of certain metabolites. These intra-system reactions can be classified as either being transformations or translocations. A transformation is a reaction that contains distinct sets of compounds as substrates and products, while a translocation contains reactants located in different compartments. Thus, a reaction that simply transports a metabolite from the extracellular environment to the cytosol, without changing its chemical composition is solely classified as a translocation, while a reaction such as the phosphotransferase system (PTS) which takes extracellular glucose and converts it into cytosolic glucose-6-phosphate is a translocation and a transformation.

[0044] Exchange reactions are those which constitute sources and sinks, allowing the passage of metabolites into and out of a compartment or across a hypothetical system boundary. These reactions are included in a model for simulation purposes and represent the metabolic demands placed on S. cerevisiae. While they may be chemically balanced in certain cases, they are typically not balanced and can often have only a single substrate or product. As a matter of convention the exchange reactions are further classified into demand exchange and input/output exchange reactions.

[0045] The metabolic demands placed on the S. cerevisiae metabolic reaction network can be readily determined from the dry weight composition of the cell which is available in the published literature or which can be determined experimentally. The uptake rates and maintenance requirements for S. cerevisiae can be determined by physiological experiments in which the uptake rate is determined by measuring the depletion of the substrate. The measurement of the biomass at each point can also be determined, in order to determine the uptake rate per unit biomass. The maintenance requirements can be determined from a chemostat experiment. The glucose uptake rate is plotted versus the growth rate, and the y- intercept is interpreted as the non-growth associated maintenance requirements. The growth associated maintenance requirements are determined by fitting the model results to the experimentally determined points in the growth rate versus glucose uptake rate plot.

[0046] Input/output exchange reactions are used to allow extracellular reactants to enter or exit the reaction network represented by a model of the invention. For each of the extracellular metabolites a corresponding input/output exchange reaction can be created.

These reactions can either be irreversible or reversible with the metabolite indicated as a substrate with a stoichiometric coefficient of one and no products produced by the reaction.

This particular convention is adopted to allow the reaction to take on a positive flux value (activity level) when the metabolite is being produced or removed from the reaction network and a negative flux value when the metabolite is being consumed or introduced into the reaction network. These reactions will be further constrained during the course of a simulation to specify exactly which metabolites are available to the cell and which can be excreted by the cell.

[0047] A demand exchange reaction is always specified as an irreversible reaction containing at least one substrate. These reactions are typically formulated to represent the production of an intracellular metabolite by the metabolic network or the aggregate production of many reactants in balanced ratios such as in the representation of a reaction that leads to biomass formation, also referred to as growth. As set forth in the Examples, the biomass components to be produced for growth include L-Alanine, L-Arginine, L- Asparagine, L-Aspartate, L-Cysteine, L-Glutamine, L-Glutamate, Glycine, L-Histidine, L- Isoleucine, L-Leucine, L-Lysine, L-Methionine, L-Phenylalanine, L-Proline, L-Serine, L- Threonine, L-Tryptophan, L-Tyrosine, L-Valine, AMP, GMP, CMP, UMP, dAMP, dCMP, dTMP, dGMP, Glycogen, alpha, alpha-Trehalose, Mannan, beta-D-Glucan, Triacylglycerol, Ergosterol, Zymosterol, Phosphatidate, Phosphatidylcholine, Phosphatidylethanolamine, Phosphatidyl-D-myo-inositol, Phosphatidylserine, ATP, Sulfate, ADP and Orthophosphate, with exemplary values shown in Table 1.

Table 1. Cellular components of S. cerevisiae (mmol/gDW). ALA 0.459 CMP 0. 05 ARG 0.161 dAMP 0. 0036 ASN 0.102 dCMP 0-0024 ASP 0.297 dGMP 0.0024 CYS 0.007 DTMP 0. 0036 GLU 0.302 TAGLY 0. 007 GLN 0.105 ERGOST 0.0007 GLY 0.290 ZYMST 0.015 HIS 0.066 PA 0.0006 ILE 0.193 PINS 0.005 LEU 0.296 PS 0.002 LYS 0.286 PE 0.005 MET 0.051 PC 0.006 GLYCOGE PHE 0.134 N 0.519 PRO 0.165 TRE 0.023 SER 0. 185 Mannan 0.809 13GLUCA THR 0. 191 N 1.136 TRP 0. 028 SLF 0. 02 TYR 0.102 ATP 23. 9166 VAL 0.265 ADP 23.9166 AMP 0.051 PI 23.9456 GMP 0. 051 Biomass 1 UMP 0. 067 [0048] A demand exchange reaction can be introduced for any metabolite in a model of the invention. Most commonly these reactions are introduced for metabolites that are required to be produced by the cell for the purposes of creating a new cell such as amino acids, nucleotides, phospholipids, and other biomass constituents, or metabolites that are to be produced for alternative purposes. Once these metabolites are identified, a demand exchange reaction that is irreversible and specifies the metabolite as a substrate with a stoichiometric coefficient of unity can be created. With these specifications, if the reaction is active it leads to the net production of the metabolite by the system meeting potential production demands. Examples of processes that can be represented as a demand exchange reaction in a reaction network data structure and analyzed by the methods of the invention include, for example, production or secretion of an individual protein; production or secretion of an individual metabolite such as an amino acid, vitamin, nucleoside, antibiotic or surfactant; production of ATP for extraneous energy requiring processes such as locomotion; or formation of biomass constituents.

[0049] In addition to these demand exchange reactions that are placed on individual metabolites, demand exchange reactions that utilize multiple metabolites in defined stoichiometric ratios can be introduced. These reactions are referred to as aggregate demand exchange reactions. An example of an aggregate demand reaction is a reaction used to simulate the concurrent growth demands or production requirements associated with cell growth that are placed on a cell, for example, by simulating the formation of multiple biomass constituents simultaneously at a particular cellular growth rate.

[0050] A hypothetical reaction network is provided in Figure 1 to exemplify the above- described reactions and their interactions. The reactions can be represented in the exemplary data structure shown in Figure 2 as set forth below. The reaction network, shown in Figure 1, includes intrasystem reactions that occur entirely within the compartment indicated by the shaded oval such as reversible reaction R2 which acts on reactants B and G and reaction R3 which converts one equivalent of B to two equivalents of F. The reaction network shown in Figure 1 also contains exchange reactions such as input/output exchange reactions At and EXt, and the demand exchange reaction, Vgrowth, which represents growth in response to the one equivalent of D and one equivalent of F. Other intrasystem reactions include Rl which is a translocation and transformation reaction that translocates reactant A into the compartment and transforms it to reactant G and reaction R6 which is a transport reaction that translocates reactant E out of the compartment.

[0051] A reaction network can be represented as a set of linear algebraic equations which can be presented as a stoichiometric matrix S, with S being an m x n matrix where m corresponds to the number of reactants or metabolites and n corresponds to the number of reactions taking place in the network. An example of a stoichiometric matrix representing the reaction network of Figure 1 is shown in Figure 2. As shown in Figure 2, each column in the matrix corresponds to a particular reaction n, each row corresponds to a particular reactant m, and each Smn element corresponds to the stoichiometric coefficient of the reactant m in the reaction denoted n. The stoichiometric matrix includes intra-system reactions such as R2 and R3 which are related to reactants that participate in the respective reactions according to a stoichiometric coefficient having a sign indicative of whether the reactant is a substrate or product of the reaction and a value correlated with the number of equivalents of the reactant consumed or produced by the reaction. Exchange reactions such as-Ext and-Axt are similarly correlated with a stoichiometric coefficient. As exemplified by reactant E, the same compound can be treated separately as an internal reactant (E) and an external reactant (EcxternaO such that an exchange reaction (R6) exporting the compound is correlated by stoichiometric coefficients of-1 and 1, respectively. However, because the compound is treated as a separate reactant by virtue of its compartmental location, a reaction, such as R5, which produces the internal reactant (E) but does not act on the external reactant (Eextcmay is correlated by stoichiometric coefficients of 1 and 0, respectively. Demand reactions such as Vgrowth can also be included in the stoichiometric matrix being correlated with substrates by an appropriate stoichiometric coefficient.

[0052] As set forth in further detail below, a stoichiometric matrix provides a convenient format for representing and analyzing a reaction network because it can be readily manipulated and used to compute network properties, for example, by using linear programming or general convex analysis. A reaction network data structure can take on a variety of formats so long as it is capable of relating reactants and reactions in the manner exemplified above for a stoichiometric matrix and in a manner that can be manipulated to determine an activity of one or more reactions using methods such as those exemplified below. Other examples of reaction network data structures that are useful in the invention include a connected graph, list of chemical reactions or a table of reaction equations.

[0053] A reaction network data structure can be constructed to include all reactions that are involved in S. cerevisiae metabolism or any portion thereof. A portion of S. cerevisiae metabolic reactions that can be included in a reaction network data structure of the invention includes, for example, a central metabolic pathway such as glycolysis, the TCA cycle, the PPP or ETS; or a peripheral metabolic pathway such as amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, vitamin or cofactor biosynthesis, transport processes and alternative carbon source catabolism. Examples of individual pathways within the peripheral pathways are set forth in Table 2, including, for example, the cofactor biosynthesis pathways for quinone biosynthesis, riboflavin biosynthesis, folate biosyntheis, coenzyme A biosynthesis, NAD biosynthesis, biotin biosynthesis and thiamin biosynthesis.

[0054] Depending upon a particular application, a reaction network data structure can include a plurality of S. cerevisiae reactions including any or all of the reactions listed in Table 2. Exemplary reactions that can be included are those that are identified as being required to achieve a desired S. cerevisiae specific growth rate or activity including, for example, reactions identified as ACOI, CDC19, CIT1, DAL7, ENO1, FBA1, FBP1, FUM1, GND1, GPM1, HXK1, ICL1, IDH1, IDH2, IDP1, IDP2, IDP3, KGD1, KGD2, LPD1, LSC1, LSC2, MDH1, MDH2, MDH3, MLS1, PDC1, PFK1, PFK2, PGI1, PGK1, PGM1, PGM2, PYC1, PYC2, PYK2, RKI1, RPE1, SOL1, TALI, TDH1, TDH2, TDH3, TKL1, TPI1, ZWF1 in Table 2. Other reactions that can be included are those that are not described in the literature or genome annotation but can be identified during the course of iteratively developing a S. cerevisiae model of the invention including, for example, reactions identified as MET62, MNADC, MNADD1, MNADE, MNADF-1, MNADPHPS, MNADG1, MNADG2, MNADH, MNPT1.

Table 2 Locus &num E. C. # Gene Gene Descriptlon Reactlon Rsn Name Carbohydrate Metabolism Glycolysls/Cluconeogenesis YCL040W 2. 7. 1. 2 GLKI Glucokinase GLC+ATP->G6P+ADP glkl_1 YCL040W 2. 7. 1. 2 GLKI Glucokinase MAN+ATP->MAN6P+ADP alk1_2 YCL040W 2. 7. 1. 2 GLKI Glucokinase bDGLC+ATP->bDG6P+ADP g) k) 3 YFR053C 2. 7.1.1 HXK1 Hexokinase I (PI) (also called hexokinase A) bDGLC+ATP->+G6P+ADP hxk1_1 YFR053C 2. 7.1.1 HXK1 Hexokinase I (PI) (also called hexokinase A) GLC+ATP->G6P+ADP hxk1_2 YFR053C 2. 7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) MAN+ATP->MAN6P+ADP hxk1_3 YFR053C 2. 7.1.1 HXK1 Hexokinase I (PI) (also called Hexokinase A) ATP+FRU->ADP+F6P hxk1_4 YGL253W 2. 7. 1. 1 HXK2 Hexokinase 11 (Pll) (also called Hexokinase B) bDGLC + ATP-> G6P + ADP hxk2 1 YGL253W 2. 7.1.1 HXK2 Hexokinase 11 (Pll) (also called Hexokinase B) GLC + ATP-> G6P + ADP hxk2 2 YGL253W 2. 7. 1. I HXK2 Hexokinase II (PII) (also called Hexokinase B) MAN+ATP->MAN6P+ADP hxk2_3 YGL253W 2. 7. 1. 1 HXK2 hexokinase II (PII) (also called hexokinase B) ATP+FRU->ADP+F6P hxk2_4 YBR196C 5. 3. 1. 9 PGI1 Glucose4-phosphaeisomerase G6P-F6P pgil I YBR196C 5.3. 1. 9 PGII Glucose-6-phosphate isomerase G6P<->bDG6P pgi1_2 YBR196C 5. 3. 1. 9 PGII Glucose-6-phosphate isomerase bDG6P<->F6P pgi1_3 YMR205C 2. 7. 1. 11 PFK2 phosphofructokinase beta subunit F6P + ATP-> FDP + ADP pfk2 YGR240C 2. 7.1.11 PFK1 phosphofructokinase alpha subunit F6P+ATP->FDP+ADP pfk1_1 YGR240C 2. 7.1.11 PFK1 phosphofructokinase alpha subunit ATP+TAG6P->ADP+TAG16P pfk1_2 YGR240C 2. 7.1.11 PFK1 phosphofructokinase alpha subunit ATP+S7P->ADP+S17P pfk1_3 YKL060C 4. 1. 2. 13 FBAI fnuctose-bisphosphatealdolase FDP<->T3P2+T3PI fbal I YDR050C 5. 3.1.1 TPI1 triosephosphate isomerase T3P2<->T3P1 tpi1 YJL052W 1.2.1.12 TDH1 Glyceraldehyde-3-phosphate dehydrogenase 1 T3P1+PI+NAD<->NADH+13PDG tdh1 YJR009C 1. 2. 1. 12 TDH2 glyceraldehyde 3-phasphate dehydrogenase T3P1+PI+NAD<->NADH+13PDG tdh2 YGR192C 1. 2. 1. 12 TDH3 Glyceraldehyde-3-phosphatedchydrogenase3 T3PI +PI+NAD<-> NADH+ 13PDG tdh3 YCR012W 2.7. 2.3 PGKI phosphoglycerate kinase 13PDG+ADP<->3PG+ATP pgk1 YKL152C 5.4. 2.1 GPM1 Phosphoglycerate mutase 13PDG<->23PDG gpm1_1 YKL152C 5.4. 2.1 GPM1 Phosphoglycerate mutase 3PG<->2PG gpm1_2 YDL021W 5.4. 2. 1 GPM2 Similar to GPMI (phosphoglycerate mutase) 3PG <-> 2PG gpm2 YOL056W 5.4. 2. 1 GPM3 phosphoglycerate mutase 3PG <-> 2PG gpm3 YGR254W 4. 2.1.11 ENO1 enolase 1 2PG<->PEP enol YHR174W 4. 2. 1. 11 EN02 enolase 2PG<->PEP eno2 YMR323W 4. 2. 1. 11 ERRI Protein with similarity to enolases 2PG <-> PEP eno3 YPL281C 4. 2. 1. 11 ERR2 enolase related protein 2PG <-> PEP eno4 YOR393W 4. 2. 1. 11 ERRI enolase related protein 2PG <-> PEP eno5 YAL038W 2. 7. 1. 40 CDC19 Pyruvate kinase PEP+ADP-gt;PYR+ATP cdc19 YOR347C 2. 7. 1. 40 PYK2 Pyruvate kinase, glucose-repressed isoform PEP + ADP-> PYR + ATP pyk2 YER178w 1. 2. 4. 1 PDAI pyruvatedehydrogenase (lipoamide) alphachain PYRm+COAm+NADm->NADHm+C02m+ pdal precursor, El component, alpha unit ACCOAm YBR221c 1. 2. 4. 1 PDBI pyruvate dehydrogenase (lipoamide) beta chain precursor, El component, beta unit YNL071w 2. 3. 1. 12 LATI dihydrolipoamide S-acetyltransferase, E2 component Cltrate cycle (TCA cycle) YNROOIC 4. 1. 3.7 CITI Citrate synthase, Nuclear encoded mitochondrial ACCOAm + OAm a COAm + CITm citl protein.

YCR005C 4. 1. 3.7 CIT2 Citrate synthase, non-mitochondrial citrate synthase ACCOA + OA-> COA + CIT cit2 YPR001 W 4. 1. 3.7 cit3 Citrate synthase, Mitochondrial isofonn of citrate ACCOAm + OAm-> COAm + ClTm ciß synthase YLR304C 4. 2. 1. 3 acol Aconitase, mitochondrial CITm <-> ICITm acol YJL200C 4. 2. 1. 3 YJL200C aconitate hydratase homolog CITm<->ICITm aco2 YNL037C 1. 1. 1. 41 IDHI Isocitratedehydrogenase (NAD+) mito, subuintl ICITm+NADm->C02m+NADHm+AKGm idhl YOR136W 1. 1. 1. 41 IDH2 Isocitrate dehydrogenase (NAD+) mito, subunit2 YDL066W 1. 1. 1. 42 IDPI Isocitratedehydrogenase (NADP+) ICITm+NADPm->NADPHm+OSUCm idpl_I YLR174W 1.1.1.42 IDP2 Isocitrate dehydrogenase (NADP+) ICIT+NADP->NADPH+OSUC idp2_1 YNL009W 1. 1. 1. 42 IDP3 Isocitratedehydrogenase (NADP+) ICIT+NADP->NADPH+OSUC idp3_1 YDL066W 1. 1. 1. 42 IDPI Isocitratedehydrogenase (NADP+) OSUCm->CO2m+AKGm idpl_2 YLR174W 1.1.1.42 IDP2 Isocitrate dehydrogenase (NADP+) OSUC->C02+AKG idp22 YNL009W 1. 1. 1. 42 IDP3 Isocitrate dehydrogenase (NADP+) OSUC->C02+AKG idp32 YIL125W 1. 2.4. 2 kgdl alpha-ketoglutaratedehydrogenasecomplex, El AKGm+NADm+COAm->C02m+NADHm+ kgdla component SUCCOAm YDR148C 2.3. 1. G1 KGD2 Dihydrolipoamide S-succinyltransferase, E2 component YGR244C 6. 2. 1. 4/6. LSC2 Succinate-CoA ligase (GDP-forming) ATPm+SUCCm+COAm<->ADPm+PIm+ Isc2 2. 1. 5 SUCCOAm YOR142W 6. 2. 1. 4/6. LSCI succinate-CoA ligase alpha subunit ATPm+ITCm+COAm<->ADPm+PIm+ Isc1 2. 1. 5 ITCCOAm Electron Transport System. Complex II YKL141w 1. 3. 5. 1 SDH3 succinate dehydrogenase cytochrome b SUCCm + FADm <-> FUMm + FADH2m sdh3 YKL148c L3. 5. 1 SDHI succinatedehydrogenasecytochromeb YLL041c 1. 3.5. 1 SDH2 Succinatedehydrogenase (ubiquinone) iron-sulfur protein subunit YDR178w 1. 3. 5. 1 SDH4 succinate dehydrogenase membrane anchor subunit YLR164w 1. 3. 5. 1 YLR164 strongsimilaritytoSDH4P w YMR118c 1. 3. 5. 1 YMRI 8 strong similarity to succinate dehydrogenase c YJL045w 1. 3. 5. 1 YJL045w strong similarity to succinate dehydrogenase flavoprotein YEL047c 1. 3. 99. 1 YEL047c soluble fumarate reductase, cytoplasmic FADH2m + FUM-> SUCC + FADm frdsl YJR051W 1. 3. 99. 1 osml Mitochondrial soluble fumarate reductase involved in FADH2m + FUMm-> SUCCm + FADm osml osmotic regulation YPL262W 4. 2. 1. 2 FUM1 Fumaratase FUMm<->MALm fuml I YPL262W 4. 2. 1. 2 FUMI Fumaratase FUM<->MAL fum1_2 YKL085W 1.1.1 37 MDHI mitochondrial malatedehydrogenase MALm+NADm<->NADHm+OAm mdhl YDL078C 1. 1. 1. 37 MDH3 MALATEDEHYDROGENASE, PEROXISOMAL MAL+NAD<->NADH+OA mdh3 YOL126C 1. 1. 1. 37 MDH2 malatedehydrogenase, cytoplasmic MAL+NAD<->NADH+OA mdh2 Anaplerotic Reactions YER065C 4. 1. 3. 1 ICLI isocitratelyase ICIT->GLX+SUCC icll YPR006C 4. 1. 3. 1 ICL2 Isocitrate lyase, may be nonfunctional ICIT-> GLX + SUCC icl2 YIR03 I C 4. 1. 3.2 dal7 Malate synthase ACCOA + GLX-> COA + MAL dal7 YNL117W 4. 1. 3.2 MLSI Malatesynthase ACCOA+GLX->COA+MAL misl YKR097W 4. 1. 1. 49 pck1 phosphoenolpyruvate carboxylkinase OA+ATP->PEP+CO2+ADP pck1 YLR377C 3. 1.3.11 FBP1 fructose-1, 6-bisphosphatase FDP->F6P+P1 fhp1 YGL062W 6.4. 1. 1 PYC1 pyruvate carboxylase PYR+ATP+CO2->ADP+OA+PI pyc1 YBR218C 6. 4.1.1 PYC2 pyruvate carboxylase PYR+ATP+CO2->ADP+OA+PI pyc2 YKL029C 1. 1. 1. 38 MAEI mitochondrialmalicenzyme MALm+NADPm->C02m+NADPHm+PYRm mael Pentose phosphate cycle YNL241C 1. 1. 1. 49 zwfl Glucose-6-phosphate-1-dehydrogenase G6P+NADP<->D6PGL+NADPH zwfl YNR034W 3. 1. 1. 31 SOLI Possible 6-phosphogluconolactonase D6PGL-> D6PGC soll YCR073W-3. 1. 1. 31 SOL2 Possible6-phosphogluconolactonase D6PGL->D6PGC sol2 A YHR163W 3. 1. 1. 31 SOL3 Possible6-phosphogluconolactonase D6PGL->D6PGC sol3 YGR248W 3. 1. 1. 31 SOL4 Possible6-phosphogluconolactonase D6PGL->D6PGC sol4 YGR256W 1.1.1. 44 GND2 6-phophogluconate dehydrogenase D6PGC+NADP->NADPH+CO2+RL5P gnd2 <BR> <BR> YHR183W 1. 1. 1. 44 GNDI 6-phophogluconatedehydrogenase D6PGC+NADP->NADPH+C02+RLSP gndl YJL121C 5. 1. 3.1 RPE1 ribulose-5-P 3-epimerase RL5P<->X5P rpc1 YOR095C 5. 3. 1. 6 RKII ribose-5-P isomerase RLSP <-> RSP rkil YBR117C 2. 2. 1. 1 TKL2 transketolase R5P+X5P<->T3P1+S7P tkl2_1 YBR117C 2. 2. 1. 1 TKL2 transketolase X5P+E4P<->F6P+T3P1 tkl2_2 YPR074C 2. 2. 1. 1 TKLI transketolase R5P+X5P<->T3P1 +S7P tkll_l YPR074C 2. 2. 1. 1 TKLI transketolase X5P+E4P<->F6P+T3Pl tkll 2 YLR354C 2.2. 1. 2 TALI transaldolase T3P1+S7P<->E4P+F6P tal1_1 YGR043C 2. 2. 1. 2 YGR043 transaldolase T3P1+S7P<->E4P+F6P tal1_2 C YCR036W 2. 7. 1. 15 RBKI Ribokinase RIB+ATP->RSP+ADP rbkl_I YCR036W 2. 7. 1. 15 RBKI Ribokinase DRIB+ATP->DR5P+ADP tbk1_2 YKL127W 5. 4.2. 2 pgml phosphoglucomutase BIP<->R5P pgm1_1 YKL127W 5. 4.2. 2 pgml phosphoglucomutase 1 GIP<->G6P pgm1_2 YMR105C 5. 4.2. 2 pgm2 phosphoglucomutase RIP<->R5P$pgm2_1 YMRIOSC 5. 4.2. 2 pgm2 Phosphoglucomutase GIP<->G6P pgm2 2 Mannose YER003C 5. 3. 1. 8 PM140 mannose-6-phosphate isomerase MAN6P <-> F6P pmi40 YFL045C 5. 4.2. 8 SEC53 phosphomannomutase MAN6P <-> MANIP sec53 YDL055C 2.7. 7. 13 PSAI mannose-1-phosphate guanyltransferase. GDP-mannose GTP + MANIP-> PPI + GDPMAN psal pyrophosphorylase Fructose YIL107C 2. 7.1.105 PFK26 6-Phosphofructose-2-kinase ATP+F6P->ADP+F26P pfk26 YOL136C 2. 7. 1. 105 pfk27 6-phosphofructo-2-kinase ATP + F6P-> ADP + F26P pfk27 YJL155C 3. 1. 3.46 FBP26 Fructose-2, 6-biphosphatase F26P->F6P+PI fbp26 2. 7. 1. 56-I-Phosphofructokinase (Fructose I-phosphate kinase) F1 P + ATP-> FDP + ADP frc3 Sorbose S. c. does not metabolize sorbitol, erythritol, mannitol, xylitol, ribitol, arabinitol, galactinol YJR) 1. 1. 1. 14 SORI sorbitol dehydrogenase (L-iditol 2-dehydrogenase) SOT+NAD->FRU+NADH sorl Calactose metabolism YBR020W 2. 7. 1. 6 gal1 galactokinase GLAC+ATP->GALIP+ADP gal1 YBR018C 2.7. 7. 10 gal7 galactose-1-phosphate uridyl transferase UTP+GALIP<->PPI+UDPGAL gal7 YBR019C 5.1. 3.2 gallO UDP-glucose 4-epimerase UDPGAL<-> UDPG gal10 YHL012W 2.7. 7.9 YHL012 UTP-Glucose 1-Phosphate Uridylyltransferase GIP+UTP<->UDPG+PP1 ugp1_2 W YKL035W 2.7. 7.9 UGP1 Uridinephosphoglucose pytophosphorylase GIP+UTP<->UDPG+PP1 ugp1_1 YBR184W 3. 2. 1. 22 YBR184 Alpha-galactosidase (melibiase) MELI->GLC+GLAC mell-I W YBR184W 3.2. 1. 22 YBR184 Alpha-galactosidase (melibiase) DFUC-> GLC + GLAC mdl 2 W YBR184W 3. 2. 1. 22 YBR184 Alpha-galactosidase (melibiase) RAF->GLAC+SUC mel1_3 W YBR184W 3. 2. 1. 22 YBR184 Alpha-galactosidase (melibiase) GLACL<->MYOI+GLAC mell 4 W YBR184W 3.2. 1. 22 YBR184 Alpha-galactosidase (melibiase) EPM<->MAN+GLAC mell 5 W YBR184W 3.2. 1.22 YBR184 Alpha-galactosidase (melibiase) GGL<->GL+GLAC mel1_6 w YBR184W 3.2. 1.22 YBR184 Alpha-galactosidase (melibiase) MELT<->SOT+GLAC mel1_7 W YBR299W 3.2. 1. 20 MAL32 Maltase MLT->2GLC mal32a YGR287C 3. 2. 1. 20 YGR287 putative alpha glucosidase MLT-> 2 GLC mai32b C YGR292W 3. 2. 1. 20 MAL12 Maltase MLT->2GLC mall2a YIL172C 3.2. 1. 20 YIL172C putative alpha glucosidase MLT->2 GLC mel128 YJL216C 3.2. 1. 20 YJL216C probable alpha-glucosidase (MALTase) MLT-> 2 GLC mat) 2c YJL221C 3. 2. 1. 20 FSP2 homologytomaltase (alpha-D-glucosidase) MLT->2 GLC fsp2a YJL22) C 3.2. 1. 20 FSP2 homology to maltase (alpha-D-glucosidase) 6DGLC-> GLAC + GLC fsp2b YBR08C 2.7. 7. 12 GAL7 UDPglucose-hexose-1-phosphate uridylyltransferase UDPG+GALIP<->GIP_UDPGAL unkrx10 Trehalose YBR) 26C 2. 4.1.15 TPS1 trchalose-6-P synthctase, 56 kD synthase subunit of UDPG+G6P->UDP+TRE6P tps1 trchalose-6-phosphate synthascVphosphatase complex YMLIOOW 2. 4. 1. 15 tsll trehalose-6-Psynthetase, 123kDregulatorysubunitof UDPG+G6P->UDP+TRE6P tsil trehalose-6-phosphate synthaseVphosphatase comples\ ; homologous to TPS3 gene product YMR261C 2. 4. 1. 15 TPS3 trehalose-6-P synthetase, 115 kD regulatory subunit of UDPG + G6P-> UDP + TRE6P tps3 trehalose-6-phosphate synthaseVphosphatase complex YDR074W 3. 1. 3. 12 TPS2 Trehalose-6-phosphate phosphatase TRE6P-> TRE + PI tps2 YPR026W 3. 2. 1. 28 ATHI Acid trehalase TRE->2 GLC athl YBROO) C 3. 2. 1. 28 NTH2 Neutral trehalase, highly homologous to Nth I p TRE 2 GLC nth2 YDR001C 3. 2. 1. 28 NTHI neutraltrehalase TRE->2GLC nthl Glycogen Metabolism (sucorose and sugar metabolism) YEL011W 2. 4. 1.18 glc3 Branching enzyme. 1.4-glucan-6-(1,4-glucano)- GLYCOGEN+PI->GIP glc3 transferase YPRI60W 2. 4.1.1 GPH1 Glycogen phosphorylase GLYCOGEN+PI->GIP gph1 YFRO15C 2. 4.1.11 GSY1 Glycogen synthase (UDP-gluocse-starch UDPG->UDP+GLYCOGEN gsy1 glucosyltransferase) YLR258W 2. 4. 1. 11 GSY2 Glycogen synthase (UDP-gluocse-starch UDPG-> UDP + GLYCOGEN gsy2 glucosyltransferase) Pyruvate Metabollsm YAL054C 6. 2. 1.1 acs1 acetyl-coenzyme A synthctase ATPm+ACm+COAm->AMPm+PPIm+ acs1 ACCOAm YLRI53C 6. 2. 1. 1 ACS2 acctyl-coenzyme A synthetase ATP+AC+COA->AMP+PPI+ACCOA acs2 YDL168W 1.2.1.1 SFA1 Formaldehyde dehydrogenase/long-chain alcohol FALD+RGT+NAD<->FGT+NADH sfa1_1 dehydrogenase YJL068C 3.1.2.12 @ S-Formylglutathione hydrolase FGT<->RGT+FOR unkrx11 YGR087C 4. 1. 1. 1 PDC6 pynuvMedecarboxylase PYR-CO2+ACAL pdc6 YLR134W 4.1.1.1 PDC5 Pyruvate decarboxylase PYR->CO2_ACAL pdc5 YLR044C 4.1.1.1 pdc1 pyruvate decarboxylase PYR->CO2+ACAL pdc1 YBLOISW 3.1.2.1 ACH1 acetyl CoA hydrolase COA+AC->ACCOA ach1_1 YBL015W 3.1.2.1 ACH1 acetyl CoA hydrolase COAm+ACm->ACCOAm ach1_2 YDL131W 4. 1. 3. 21 LYS21 probable homocitrate synthase, mitochondrial isozyme ACCOA+AKG->HCIT+COA lys21 precursor YDL182W 4. 1. 3. 21 LYS20 homocitmte synthase, cytosolic isozyme ACCOA + AKG-> HCIT + COA Iys20 YDL182W 4. 1. 3. 21 LYS20 Homocitrate synthase ACCOAm+AKGm->HCITm+COAm Iys20a YGL256W 1.1.1.1 adh4 alcohol dehydrogenase isoenzyme IV ETH+NAD<->ACAL+NADH adh4 YMR083W 1. 1. 1. 1 adh3 alcohol dehydrogenase isoenzyme 111 ETHm + NADm <-> ACALm + NADHm adh3 YMR303C 1. 1. 1. 1 adh2 alcohol dehydrogenase 11 ETH + NAD <-> ACAL + NADH adh2 YBR145W 1.1.1.1 ADH5 alcohol dehydrogenase isoenzyme V ETH+NAD<->ACAL+NADH adh5 <BR> <BR> YOL086C 1. 1. 1. 1 adhl Alcoholdehydrogenasel ETH+NAD<->ACAL+NADH adhl YDL168W 1.1.1.1 SFA1 Alcohol dehydrogenase 1 ETH+NAD<->ACAL+NADH sfa1_2 Glyoxylate and dlcarboxylate metabollsm ClyoxalPathway YML004C 4. 4.1.5 GLO1 Lactoylglutathione lyase, glyoxalase 1 RGT+MTHGXL<->LGT glo1 YDR272W 3. 1. 2.6 GL02 Hydroxyacylglutathione hydrolase LGT->RGT+LAC glo2 YOR040W 3. 1. 2. 6 GL04 glyoxalase II (hydroxyacylglutathione hydrolase) LGTm->RGTm+LACm glo4 Energy Metabolism Oxidative Phosphorylation YBROI IC 3. 6. 1. 1 ippl Inorganicpyrophosphatase PPI->2P1 ippl YMR267W 3. 6. 1. 1 ppa2 mitochondrial inorganic pyrophosphatase PPIm-> 2 Plm ppa2 1. 2. 2. 1 FDNG Formate dehydrogenase FOR + Qm-> QH2m + C02 +2 HEXT fdng YMLI 20C 1. 6. 5. 3 NDII NADH dehydrogenase (ubiquinone) NADHm+Qm->QH2m+NADm ndil YDL085W 1. 6. 5. 3 NDH2 Mitochondrial NADH dehydrogenase that catalyzes the NADH + Qm-> QH2m + NAD ndh2 oxidation of cytosolic NADH YMR145C 1. 6. 5. 3 NDH1 Mitochondrial NADH dehydrogenase that catalyzes the NADH+Qm->QH2m+NAD ndh1 oxidation of cytosolic NADH YHR042W 1. 6.2. 4 NCP1 NADPH-ferrihemoprotein reductase NADPH+2FERIm->NADP+2FEROm ncp1 YKL141w 1. 3. 5. 1 SDH3 succinate dehydrogenase cytochrome b FADH2m + Qm <-> FADm + QH2m fad YKL148c 1. 3. 5. 1 SDHI succinate dehydrogenase cytochrome b YLL041c 1. 3. 5. 1 SDH2 succinate dehydrogenase cytochrome b YDR178w 1. 3. 5. 1 SDH4 succinate dehydrogenase cytochrome b Electron Transport System. Complex III YEL024W 1. 10. 2.2 RIPI ubiquinol-cytochrome c reductase iron-sulfur subunit 02m + 4 FEROm + 6 Hm-> 4 FERIm cyto Q0105 1. 10. 2.2 CYTB ubiquinol-cytochrome c reductase cytochrome b subunit YOR065W 1. 10. 2.2 CYT1 ubiquinol-cylochrome c reductase cytochrome cl subunit YBL045C 1. 10. 2.2 CORI ubiquinol-cytochrome c reductase core subunit I YPR191W 1.10 2.2 QCRI ubiquinol-cytochrome c reductase core subunit 2 YPR191W 1.10. 2.2 QCR2 ubiquinol-cytochrome c reductase YFR033C 1. 10. 2.2 QCR6 ubiquinol-cytochrome c reductase subunit 6 YDR529C 1. 10. 2.2 QCR7 ubiquinol-cytochrome c reductase subunit 7 YJL166W 1. 10. 2.2 QCR8 ubiquinol-cytochrome c reductase subunit 8 YGR183C 1.10. 2.2 QCR9 ubiquinol-cytochrome c reductase subunit 9 YHROOIW-1. 10. 2.2 QCR10 ubiquinol-cytochrome c reductase subunit 10 A Electron Transport System, Complex IV Q0045 1. 9. 3.1 COX1 cytochrome c oxidase subunit I QH2m+2FERIm+1.5Hm-gt;Qm+2FEROm cytr Q0250 1. 9. 3. 1 COX2 cytochrome c oxidase subunit I Q0275 1. 9.3. 1 COX3 cytochrome c oxidase subunit I YDL067C 1. 9.3. 1 COX9 cytochrome c oxidase subunit I YGL187C 1. 9.3. 1 COX4 cytochrome c oxidase subunit I YGL19IW 1. 9. 3. 1 COX13 cytochrome c oxidase subunit I YHR051W 1. 9. 3. 1 COX6 cytochrome c oxidase subunit I YIL111W 1.9.3.1 COX5B cytochrome c oxidase subunit I YLR038C 1. 9. 3. 1 COX 12 cytochrome c oxidase subunit I YLR395C 1.9. 3. 1 COX8 cytochrome c oxidase subunit I YMR256C 1. 9. 3. 1 COX7 cytochrome c oxidase subunit I YNL052W 1. 9. 3. 1 COX5A cytochrome c oxidase subunit I A TP Syalhase YBL099W 3. 6. 1. 34 ATPI FIFO-ATPasecomplex, Fl alpha subunit ADPm++ m->ATPm+3Hm atpl YPL271W 3.6. 1. 34 ATP) F F0-ATPasecomplex, Flepsilonsubunit YDL004W 3. 6. 1. 34 ATP16 F-type H+-transporting ATPase deita chain Q0085 3. 6. 1. 34 ATP6 FIFO-ATPase complex, FO A subunit YBR039W 3. 6. 1. 34 ATP3 FIFO-ATPase complex, FI gamma subunit YBR127C 3.6. 1. 34 VMA2 H+-ATPase V I domain 60 KD subunit, vacuolar YPL078C 3.6. 1. 34 ATP4 FIFO-ATPasecomplex, Fldeltasubunit YDR298C 3. 6. 1. 34 ATP5 FIFO-ATPase complex, OSCP subunit YDR377W 3. 6. 1. 34 ATP17 ATP synthase complex, subunit f YJR121 W 3. 6. 1. 34 ATP2 FIFO-ATPase complex, Fl beta subunit YKL016C 3. 6. 1. 34 ATP7 Fl FO-ATPase complex, FO D subunit YLR295C 3. 6. 1. 34 ATP14 ATP synthase subunit h Q0080 3.6. 1. 34 ATP8 F-type H+-transporting ATPase subunit 8 Q0130 3. 6. 1. 34 ATP9 F-type H+-transporting ATPase subunit c YOL077W-3.6. 1. 34 ATP19 ATP synthase k chain, mitochondrial A YPR020W 3. 6. 1. 34 ATP20 subunit G of the dimeric form of mitochondrial FIFE- ATP synthase YLR447C 3.6. 1. 34 VMA6 V-type H+-transporting ATPase subunit AC39 YGR020C 3.6. 1. 34 VMA7 V-type transporting ATPase subunit F YKL080W 3.6. 1. 34 VMA5 V-type H+-transporting ATPase subunit C YDL185W 3.6. 1. 34 TFPI V-type H+-transporting ATPase subunit A YBR127C 3.6. 1. 34 VMA2 V-type H+-transporting ATPase subunit B YOR332W 3.6. 1. 34 VMA4 V-type H+-transporting ATPase subunit E YEL027W 3.6. 1. 34 CUP5 V-type H+-transporting ATPase proteolipid subunit YHR026W 3. 6. 1. 34 PPA I V-type H+-transporting ATPase proteolipid subunit YPL234C 3. 6. 1. 34 TFP3 V-type H+-transporting ATPase proteolipid subunit YMR054W 3. 6. 1. 34 STV I V-type H+. transporting ATPase subunit I YOR270C 3. 6. 1. 34 VPH1 V-type H+-transporting ATPase subunit 1 YEL05 I W 3.6. 1.34 VMA8 V-type H+-transporting ATPase subunit D YHR039C-A 3.6. 1. 34 VMA10 vacuolar ATP synthase subunit G YPR036W 3.6. 1. 34 VMA13 V-type H+-transporting ATPase 54 kD subunit Electron Transport System. Complex IV Q0045 1. 9.3. 1 COX1 cytochrome-c oxidase subunit I 4FEROm+O2m+6Hm->4FERIm cox1 Q0275 1. 9.3. 1 COX3 Cytochrome-c oxidase subunit III, mitochondrially- coded Q0250 1. 9.3. 1 COX2 cytochrome-c oxidase subunit 11 YDL067C 1. 9.3. 1 COX9 Cytochrome-c oxidase YGL187C 1. 9.3. 1 COX4 cytochrome-c oxidase chain IV YGL19IW 1. 9.3. 1 COX13 cytochrome-c oxidase chain Via YHROSIW 1. 9. 3. 1 COX6 cytochrome-c oxidase subunit VI YLR395C 1.9. 3.1 COX8 cytochrome-c oxidase chain VIII YMR256C 1. 9. 3. 1 COX7 cytochrome-c oxidase, subunit VII YNL052W 1. 9. 3. 1 COX5A cytochrome-c oxidase chain V. A precursor YML054C 1. 1. 2.3 cyb2 Lactic acid dehydrogenase 2 FERIm+LLACm->PYRm+2FEROm cyb2 YDL174C 1. 1. 2.4 DLDI mitochondrial enzyme D-lactate ferricytochrome c 2FERIm+LACm->PYRm+2 FEROm dldl oxidoreductase Methane metabolism YPL275W 1. 2.1. 2 YPL275 putativeformatedehydrogenase/putativepseudogene FOR+NAD->C02+NADH tfola W YPL276W 1. 2. 1. 2 YPL276 putativeformatedehydrogenase/putativepseudogene FOR+NAD->C02+NADH tfolb W YOR388C 1. 2. 1. 2 FDH1 Protein with similarity to formate dehydrogenases FOR+NAD->CO2+NADH fah1 Nitrogen metabolism YBR208C 6.3. 4.6 DURI urea amidolyase containing urea carboxylase / ATP+UREA+CO2<->ADP+PI+UREAC dur@ allophanate hydrolase YBR208C 3.5. 1.54 DUR1 Allophanate hydrolase UREAC->2NH3+2CO2 dur2 YJL126W 3.5. 5.1 NIT2 nitrilase ACNL->INAC+NH3 nit2 Sulfur metabolism (Cysteln biosynthesis maybe) YJR137C 1. 8. 7. 1 ECMt7 Sulfitereductase H2S03+3NADPH<->H2S+3NADP ecml7 Lipid Metabolism Fatty acid blosynthesis YER015W 6. 2. 1. 3 FAA2 Long-chain-fatty-acid--CoAligase, Acyl-CoA ATP+LCCA+COA<->AMP+PPI+ACOA faa2 synthetase YIL009W 6. 2. 1. 3 FAA3 Long-chain-fatty-acid--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> AMP + PPI + ACOA faa3 synthetase YOR3) 7W 6. 2. 1. 3 FAAL Long-chain-fatty-acid--CoA ligase, Acyl-CoA ATP + LCCA + COA <-> AMP + PPI + ACOA faal synthetase YMR246W 6. 2. 1. 3 FAA4 Acyl-CoA synthase (long-chain fatty acid CoA ligase) ; ATP+LCCA+COA<->AMP+PPI+ACOA faa4 contributes to activation of imported myristate YKR009C 1.1.1 @ FOX2 3-Hydroxyacyl-CoA dehydrogenase IIACOA+NAD<->OACOA+NADH fox2b YIL160C 2.3. 1. 16 potl 3-Ketoacyl-CoA thiolase OACOA + COA-> ACOA + ACCOA potl_l YPL028W 2.3. 1. 9 erg10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2ACCOA<->COA+AACCOA erg10_1 COA THIOLASE YPL028W 2. 3. 1. 9 erg10 Acetyl-CoA C-acetyltransferase, ACETOACETYL- 2ACCOAm<->COAm+AACCOAm erg10_2 COA THIOLASE (mitoch) Fatty Acids Metabolism Mitochondrial type 11 fatty acid synthase YKL192C 1. 6.5. 3 ACP1 Acyl carrier protein, component of mitochondrial type II NADHm+Qm->NADm+QH2m ACP1 fatty acid synthase YER061C-CEMI Beta-ketoacyl-ACP synthase, mitochondrial (3-oxoacyl- [Acyl-carrier-protein] synthase) YOR221C-MCTI Malonyl CoA: acyl carrier protein transferase YKLO55C-OARI 3-Oxoacyl- [acyl-carrier-protein] reductase YKL192C/Y 1.6. 5. 3/- ACPI/CE Type 11 fatty acid synthase ACACPm+4MALACPm+UNAADPHm->8 TypeII_1 ER061C/YO /-/- Ml/MCT NADPm+C100ACPm+4CO2m+4ACPm R221C/YKL I/OARI 055C YKL192C/Y I. 6.5. 3/- ACPl/CE Type II fatty acid synthase ACACPm+5MALACPm+10NADPHm->10 TypeII_2 ER061C/YO /-/- Ml/MCT NADPm+C120ACPm+5CO2m+5ACPm R221C/YKL I/OARI 055C YKH92C/Y). 6.5. 3/- ACPI/CE Type II fatty acid synthase ACACPm+6MALACPm+12NADPHm->12 TypeII_3 ER061C/YO /-/- Ml/MCT NADPm+C141ACPm+6CO2M+6ACPm R221CNKL l/OARI 055C YKL192C/Y 1. 6.5. 3/. ACPl/CE Type 11 fatty acid synthase ACACPm+6MALACPm+11NADPHm->11 TypeII_4 ER061C/YO /-/- Ml/MCT NADPm+C141ACPm+6CO2m+6ACPm R221C/YKL l/OARl 055C YKL192C/Y 1. 6. 5. 3/. ACPl/CE Type 11 fatty acid synthase ACACPm+7MALACPm+14NADPHm->14 TypeII_5 ER061C/YO /-/- Ml/MCT NADPm+C160ACPm+7CO2m+7ACPm R221C/YKL I/OARI 055C YKL192C/Y 1. 6. 5. 3/- ACPI/CE Type 11 fatty acid synthase ACACPm+7MALACPm+13NADPHm->13 TypeII_6 <BR> ER061C/YO/-/-MI/MCT NADPm+C161ACPm+7C02m +7ACPm<BR> <BR> <BR> <BR> <BR> R221C/YKL l/OARI 055C YKL192C/Y 1.6. 5. 3/- ACPl-CE Type II fatty acid synthase ACACPm+8MALACPm+16NADPHm->16 TypeII_7 ER061C/YO /-/- Ml-MCT NADPm+C180ACPm+8CO2m+8ACPm R221C/YKL l/OARI 055C YKL192C/Y 1. 6.5. 3/- ACPI/CE Type 11 fatty acid synthase ACACPm+8MALACPm+15NADPHm->15 TypeII_8 ER061C/YO /-/- Ml/MCT NADPm+C181ACPm+8CO2m+8ACPm R221C/YKL I/OARI 055C YKL192C/Y 1.6. 5. 3/- ACP1/CE Type II fatty acid synthase ACACPm+8MALACPm+14NADPHm->14 TypeII_9 <BR> ER06) C/YO/-/-M)/MCT NADPm+C) 82ACPm+8C02m+8ACPm<BR> <BR> <BR> <BR> <BR> R221CNKL I/OARI 055C Cytosolic fatty acid synthesis YNP0162 6.4.1 2 ACCI acetyl-CoA carboxylase (ACC)/biotin carboxylase ACCOA+ATP+CO2<->MALCOA+ADP+PI acc1 6.3. 4. 14 YKL182w 4. 2. 1. 61 ; fasl fatty-acyl-CoAsynthase, betachain MALCOA+ACP<->MALACP+COA fasl_I 1. 3. 1. 9 : 2.

3. 1. 38; 2.

3. 1. 39; 3.

1. 2. 14 ; 2.

3. 1. 86 YPL231w 2.3. 1. 85; FAS2 fatty-acyl-CoA synthase, alpha chain 1. 1. 1. 100 ; 2. 3. 1. 41 YKL182w 4.2. 1.61 ; fas1 fatty-acyl-CoA synthase, beta chain ACCOA+ACP<->ACACP+COA fas1_2 1. 3. 1. 9 ; 2.

3. 1. 38; 2.

3. 1. 39; 3.

1. 2. 14 ; 2.

3. 1. 86 YER06 I C 2. 3.1.42 CEM1 3-Oxoacyl-[acyl-carrier-protein] synthase MALACPm + ACACPm-> ACPm + C02m + cem I 30ACPm YGR037C/Y 6. 4. 1. 2; ACB1/A b-Ketoacyl-ACP synthase (C10.0), fatty acyl CoA ACACP + 4 MALACP + 8 NADPH-> 8 NADP + clOOsn NR016C/YK 6.3. 4. 1 ; 4 CO/fasl/synthase OOOACP+4C02+4ACP L182W/YPL 2.3.1. 85 ; FAS2/ 231w 1.1.1.100 ; 2. 3. 1. 41 ; 4. 2. 1. 61 YGR037C/Y 6.4. 1. 2; ACBl/A b-Ketoacyl-ACP synthase (C12,0), fatty acyl CoA ACACP+5MALACP+10NADPH->10NADP+ c120sn NRO16C/YK 6.3. 4. 1; 4 CC1/fas1/ synthase C120ACP+5CO2+5ACP L182W/YPL 2.3. 1. 85 ; FAS2/ 231w 1. 1. 1. 100 ; 2. 3.1.41 ; 4. 2. 1. 61 YGR037C/Y 6. 4. 1. 2; ACBl/A b-ketoacyl-ACP synthase (C14.0) ACACP+6MALACP+12NADPH->12NADP+c140sn NRO16C/YK 6.3. 4. 1 ; 4 CCI/fasl/C140ACP+6C02+6ACP H82W/YPL 2. 3. 1. 85; FAS2/ 231w 1. 1. 1. 100 ; 2. 3.1.41 ; 4.2. 1. 61 YGR037C/Y 6.4. 1.2; ACBl/A B-ketoacyl-ACP synthase 1 (C14,1) ACACP+6MALACP+11NADPH->11NADP+ c141sy NR016C/YK 6.3. 4. 1 ; 4 CC1/fas1/ C141ACP+6CO2+6ACP L182W/YPL 2. 3. 1. 85; FAS2/ 231w 1.1.1.100 ; 2.3. 1. 41 ; 4. 2. 1. 61 YGR037C/Y 6. 4. 1. 2; ACBl/A b-Ketoacyl-ACP synthase I (C16.0) ACACP+7MALACP+14NADPH->14NADP+c160sn NRO16C/YK 6.3. 4. 1 ; 4 CC)/fasl/060ACP+7C02+7ACP L182W/YPL 2. 3. 1. 85 ; FAS2/ 231w 1.1.1.100 ; 2. 3.1.41 ; 4. 2. 1. 61 YGR037C/Y 6.4. 1. 2; ACBI/A b-Ketoacyl-ACPsynthasel (C16, 1) ACACP+7MALACP+13NADPH->13NADP+ cl6lsy NR016C/YK 6.3. 4. 1 ; 4 CCl/fas1/ C161ACP+7CO2+7ACP L182W/YPL 2. 3. 1. 85 ; FAS2/ 231w 1. 1. 1. 100 ; 2. 3. 1. 41 ; 4.2. 1. 61 YGR037C/Y 6. 4. 1. 2; ACBl/A b-Ketoacyl-ACP synthase I (C18.0) ACACP+8MALACP+16NADPH->16NADP+ c180sy NR016C/YK 6.3. 4. 1 ; 4 CCI/fasl/C180ACP+8C02+8ACP L182W/YPL. 2. 3. 1. 85 ; FAS2/ 231w 1. 1. 1. 100 ; 2.3. 1.41; 4.2. 1. 61 YGR037C/Y 6. 4. 1. 2; ACB1/A b-Ketoacyl-ACP synthase I (C18,1) ACACP+8MALACP+15NADPH->15NADP+ c181sy NR016C/YK 6.3. 4. 1 ; 4 CCI/fasl/C181ACP+8 C02+8ACP L1822/YPL 2. 3. 1. 85; FAS2/ 231w 1. 1. 1. 100 ; 2. 3.1.41; 4.2. 1. 61 YGR037C/Y 6.4. 1.2 ; ACBl/A b-ketoacyl-ACP synthase I (C18, 2) ACACP+8MALACP+14NADPH->14NADP+ cl82sy NR016C/YK 6.3. 4. 1 ; 4 CC1/fas1/ C182ACP+8CO2+8ACP L182W/YPL 2. 3. 1. 85 ; FAS2/ 231w 1. 1. 1. 100 ; 2. 3. 1. 41 ; 4.2. 1. 61 YKL182W 4.2. 1.61 fas1 3-hydroxypalmitoyl-[acyl-carrier protein]dehydratase 3HPACP<->2HDACP fas1_3 YKL182W 1. 3. 1.9 fas1 Enoyl-ACP reductase AACP+NAD<->23DAACP+NADH fas1_4 Fatty acid degradation YGL205W/Y 1. 3.3. 6/2. POXI/FO Fattyaeiddegradation C140+ATP+7COA +7FADm+7NAD->AMP e140dg KR009C/Y 3. 1. 18 X2/POT3 + PPI + 7 FADH2m + 7 NADH + 7 ACCOA 160C YGL205W/Y 1. 3.3. 6/2. POXl/FO Fatty acid degradation C160 + ATP + 8 COA + 8 FADm + 8 NAD-> AMP e160dg KR009C/YIL 3. 1. 18 X2/POT3 + PPI + 8 FADH2m + 8 NADH + 8 ACCOA 160C YGL205W/Y 1. 3.3. 6/2. POUX INFO Fatty acid degradation C180 + ATP + 9 COA + 9 FADm + 9 NAD-> AMP c I80dg KR009C/YIL 3.1.18 X2/POT3 +PPI+9FADH2m+9NADH+9ACCOA <BR> <BR> 160C<BR> <BR> <BR> <BR> Phosphollpld Blosynthesis - - Glycerol-3-phosphate acyltransferase GL3P+0.017C100ACP+0.062C120ACP+0.1 GatI_1 C140ACP + 0.27 C160ACP + 0. 169 C 161 ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP-> AGL3P + ACP <BR> <BR> --Glycerol-3-phosphate acyltransforase GL3P + 0. 017 C100ACP +0. 062 C120ACP +0. 1 Gat2 1 C140ACP + 0.27 C160ACP + 0. 169C161ACP + 0.055 C180ACP+0. 235 C181ACP+0. 093 C182ACP-> AGL3P + ACP - - Glycerol-3-phosphate acyltransferase T3P2+0.017C100ACP+0.062C120ACP+0.1 GatI_2 C 140ACP + 0.27 C160ACP + 0. 169 C161ACP + 0.055 C180ACP+0. 235 C181ACP+0. 093 C 82ACP-> AT3P2 + ACP <BR> <BR> --Glycerol-3-phosphate acyltransferase T3P2 + 0. 017 CIOOACP +0. 062 C120ACP + 0. 1 Gat2 2 C140ACP + 0.27 C 160ACP + 0. 169C161ACP + 0.055 Cl 80ACP + 0. 235 C181ACP + 0. 093 C182ACP-> AT3P2 + ACP - - Acyldihydroxyacetonephosphate reductase AT3P2+NADPH->AGL3P+NADP ADHAPR YDL052C 2.3. 1. 51 SLC1 1-Acylglycerol-3-phosphate acyltransferase AGL3P+0.017C100ACP+0.062C120ACP+0.100 sk1 C 140ACP + 0.270 C 160ACP + 0. 169 C 161 ACP + 0.055 CI80ACP + 0. 235 C18IACP + 0. 093 C182ACP-> PA + ACP 2. 3.1. 51-1-Acylglycerol-3-phosphateacyltransferase AGL3P+0. 01 C100ACP+0. 062C120ACP+0. 100 AGAT C140ACP + 0.270 C160ACP + 0. 169 C 161 ACP + 0.055 C180ACP+0. 235 C181ACP + 0. 093 C182ACP-> PA + ACP YBR029C 2.7. 7. 41 CDSI CDP-Diacylglycerol synthetase PAm+CTPm<->CDPDGm+PPIm cds1a YBR029C 2.7. 7. 41 CDSI CDP-Diacylglycerol synthetase PA+CTP<->CDPDG+PPI cds1b YER026C 2.7. 8.8 chol phosphMidylserine synthase CDPDG + SER <-> CMP + PS chola YER026C 2.7. 8.8 cho1 Phosphatidylserine synthase CDPDGm+SERm<->CMPm+PSm chl1b YGR170W 4. 1. 1. 65 PSD2 phosphatidylserine decarboxylase located in vacuole or PS-> PE + C02 psd2 Golgi YNL169C 4.1. 1.65 PSDI PhosphatidylserineDecarboxylase I PSm->PEm+C02m psdl YGR157W 2.1.1.17 CHO2 Phosphatidylethanolamine N-methyltransferase SAM+PE->SAH+PMME cho2 YJR073C 2. 1. 1. 16 OP13 Methylene-fatty-acyl-phospholipid synthase. SAM + PMME-> SAH + PDME opi3 1 YJR073C 2.1.1.16 OPI3 Phosphatidyl-N-methylethanolamine N- PDME+SAM->PC+SAH opi3_2 methyltransferase YLR133W 2. 7. 1. 32 CKII Choline kinase ATP + CHO-> ADP + PCHO ckil YGR202C 2.7. 7. 15 PCTI Cholinephosphate cytidylyltransferase PCHO + CTP-> CDPCHO + PPI pctl YNL130C 2.7. 8.2 CPTI Diacylglycerol cholinephosphotransferase CDPCHO + DAGLY-> PC + CMP cptl YDR147W 2.7.1.82 EKII Ethanolamine kinase ATP+ETHM->ADP+PETHM cki1 YGR007W 2.7. 7. 14 MUQI Phosphoethanolamine cytidylyltransferase PETHM + CTP-> CDPETN + PPI ectl YHR123W 2.7. 8. 1 EPTI Ethanolaminephosphotransferase. CDPETN + DAGLY <-> CMP + PE eptl YJL153C 5. 5. 1. 4 inol myo-lnositol-l-phosphats synthase G6P-> MIIP inol YHR046C 3. 1. 3.25 INM1 myo-Inositol-1(or 4)-monophosphatase MIIP->MYOI+PI impal YPRI 13W 2.7. 8. 11 PISI phosphatidylinositol synthase CDPDG+MYOI->CMP+pINS pisl YJR066W 2. 7. 1. 137 tor1 1-Phosphatidylinositol 3-kinase ATP+PINS->ADP+PINSP tor1 YKL203C 2. 7. 1. 137 tor2 1-Phosphatidylinositol3-kinase ATP+PiNS->ADP+PiNSP tor2 YLR240W 2. 7. 1.137 vps34 1-Phosphatidylinositol 3-kinase ATP+PINS->ADP+PINSP vps34 YNL267W 2. 7. 1. 67 PIK1 Phosphatidylinositol 4-kinase (PI 4-kinase), generates ATP + PINS-> ADP + PINS4P pikl Ptdlns 4-P YLR305C 2. 7. 1. 67 STT4 Phosphatidylinositol4-kinase ATP+pINS->ADP+PtNS4P sst4 YFR019W 2. 7. 1. 68 FABI PROBABLEPHOSPHATIDYLINOSITOL-4-PINS4P+ATP->D45PI+ADP fabl PHOSPHATE 5-KINASE, I-phosphatidylinositol-4- phosphate kinase YDR208W 2. 7. 1. 68 MSS4 Phosphatidylinositol-4-phosphate 5-kinase ; required for P + ATP-> D45PI + ADP mss4 proper organization of the actin cytoskeleton YPL268W 3.1. 4. 11 plcl 1-phosphatidylinositol-4, 5-bisphosphate D45PI->TPI+DAGLY picl phosphodiesterase YCL004W 2.7. 8.8 PGSI CDP-diacylglycerol-serine0-phosphatidyltransferase CDPDGm+GL3Pm<->CMPm+PGPm pgsl -3. 1. 3.27 Phosphatidylglycerol phosphate phosphatase A PGPm-> Plm + PGm pgpa YDL142C 2.7. 8.5 CRDI Cardiolipin synthase CDPDGm + PGm-> CMPm + CLm crdl YDR284C DPPI diacylglycerolpyrophosphatephosphatase PA->DAGLY+PI dppl YDR503C LPPI lipid phosphate phosphatase DGPP->PA+PI lpp1 Sphlngoglycollpid Metabollsm YDR062W 2.3. 1. 50 LCB2 Serine C-palmitoyltransferase PALCOA+SER->COA+DHSPH+CO2 lcb2 YMR296C 2.3. 1.50 LCB1 Scrine C-palmitoyltransferase PALCOA+SER->COA+DHSPH+CO2 lcb1 <BR> <BR> YBR265w 1. 1. 1. 102 TSC10 3-Dehydrosphinganinereductase DHSPH+NADPH->SPH+NADP tscl0 YDR297W SUR2 SYRINGOMYCIN RESPONSE PROTEIN 2 SPH+O2+NADPH->PSPH+NADP sur2 <BR> <BR> Ceramide synthase PSPH + C260COA-> CER2 + COA csyna<BR> <BR> <BR> -Ceramide synthase PSPH + C240COA-> CER2 + COA csynb YMR272C SCS7 Ceramide hydroxylase that hydroxylates the C-26 fatty-CER2 + NADPH + 02-> CER3 + NADP scs7 acyl moiety of inositol-phosphorylceramide YKL004W AURI IPS synthase, AUREOBASIDIN A RESISTANCE CER3+PINS->IPC aur1 PROTEIN YBR036C CSG2 Protein required for synthesis of the mannosylated IPC + GDPMAN-> MIPC csg2 sphingolipids YPL057C SURI Protein required for synthesis of the mannosylated IPC + GDPMAN-> MIPC surl sphingolipids YDR072C 2.-.-.-IPTI MIP2C synthase, MANNOSYL MIPC + PINS-> MIP2C iptl DIPHOSPHORYLTNOSITOL CERAMIDE SYNTHASE YOR171C LCB4 Long chain base kinase, involved in sphingolipid SPH + ATP-> DHSP + ADP Icb4 1 metabolism YLR260W LCBS Long chain base kinase, involved in sphingolipid SPH+ATP->DHSP+ADP lcb5_1 metabolism YOR171C LCB4 Long chain base kinase, involved in sphingolipid PSPH + ATP-> PHSP + ADP Icb4 2 metabolism YLR260W LCB5 Long chain base kinase, involved in sphingolipid PSPH + ATP-> PHSP + ADP lcb5_2 metabolism YJL134W LCB3 Sphingoid base-phosphate phosphatase, putative DHSP->SPH+PI lcb3 regulator of sphingolipid metabolism and stress response YKR053C YSR3 Sphingoid base-phosphate phosphatase, putative DHSP-> SPH + Pi ysr3 regulator of sphingolipid metabolism and stress response YDR294C DPLI Dihydrosphingosine-1-phosphate lyase DHSP a PETHM + C16A dpil Sterol blosynthesis YMLI 26C 4. 1. 3. 5 HMGS 3-hydroxy-3-methylglutaryl coenzyme A synthase H3MCOA + COA <-> ACCOA + AACCOA hmgs YLR450W 1. 1. 1. 34 hmg2 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) MVL+COA+2NADP<->H3MCOA+2NADPH hmg2 reductase isozyme YML075C 1. 1. 1. 34 hmgl 3-hydroxy-3-methylglutaryl-coenzymeA (HMG-CoA) MVL+COA+2NADP<->H3MCOA+2NADPH hmgl reductase isozyme YMR208W 2.7. 1.36 erg12 mevalonate kinase ATP+MVL->ADP+PMVL erg12_1 YMR208W 2.7. 1.36 erg12 mevalonate kinase CTP+MVL->CDP+PMVL erg12_2 YMR208W 2. 7. 1. 36 ergl2 mevalonatekinase GTP+MVL->GDP+PMVL ergl2 3 YMR208W 2. 7. 1. 36 ergl2 mevalonate kinase UTP+MVL->UDP+PMVL erg12_4 YMR220W 2.7. 4.2 ERG8 48 kDa Phosphomevalonate kinase ATP+PMVL->AD+PPMVL erg8 YNR043W 4. 1. 1. 33 MVDI Diphosphomevalonate decarboxylase ATP+PPMVL->ADP+PI+IPPP+CO2 mvd1 YPLI 17C 5.3. 3.2 idil Isopentenyl diphosphate : dimethylallyl diphosphate IPPP <-> DMPP idil isomerase (IPP isomerase) YJL167W 2. 5. 1. 1 ERG20 prenyltransferase DMPP+IPPP->GPP+RRI erg20_1 YJL167W 2. 5. 1. 10 ERG20 Famesyl diphosphate synthetase (FPP synthetase) GPP+IPPP->FPP+PPI erg20_2 YHR190W 2.5. 1. 21 ERG9 Squalenesynthase. 2FPP+NADPH->NADP+SQL erg9 YGR175C 1. 14. 99.7 ERGI Squalenemonooxygenase SQL+02+NADP->S23E+NADPH ergl YHR072W 5.4. 99.7 ERG7 2, 3-oxidosqualene-lanosterol cyclase S23E-> LNST erg7 YHR007c 1.14.14.1 erg11 cytochrome P450 lanosterol 14a-demethylase LNST+RFP+O2->IGST+OFP erg11_1 <BR> <BR> YNL280c @ ERG24 C-14 sterol reductase IGST+NADPH->DMZYMST+NADP erg24 YGR060w).-.-.-ERG25 C-4stero) methy) oxidase 3 02 + DMZYMST-> IMZYMST erg25 1 YGLOOc 5.3. 3. 1 ERG26 C-3 sterol dehydrogenase (C-4 decarboxylase) IMZYMST->IIMZYMST+CO2 erg26_1 <BR> <BR> YLRIOOC YLRI00 C-3sterol keto reductase IIMZYMST+NADPH->MZYMST+NADP ergll 2<BR> <BR> <BR> C YGR060w ERG25 C4 sterol methyl oxidase 3O2+MZYMST->IZYMST erg25_2 YGLOOIc 5.3. 3. 1 ERG26 C-3 sterol dehydrogenase (C-4decarboxylase) IZYMST-> IIZYMST+C02 erg26 2 YLR100C YLR100 C-3 sterol keto reductase IIZYMST+NADPH->ZYMST+NADP erg11_3 C YML008c 2. 1. 1. 41 erg6 S-adenosyl-methioninedelta-24-sterol-c-ZYMST+SAM->FEST+SA H erg6 methyltransferase YMR202W ERG2 C-8 sterol isomerase FEST->EPST erg2 YLR056w 1.-.-.-ERG3 C-5 sterol desaturase EPST + 02 + NADPH-> NADP + ERTROL erg3 <BR> <BR> YMR015c 1.14.14.@ ERG5 C-22 sterol desaturase ERTOL+O2+NADPH->NADP+ERTEOL erg5<BR> <BR> <BR> YGL012w 1.@ ERG4 sterol C-24 reductase ERTEOL+NADPH->ERGOST+NADP erg4 LNST+3O2+4NADPH+NAD->MZYMST+ unkrxn3 C02 + 4 NADP + NADH MZYMST + 3 02 + 4 NADPH + NAD-> ZYMST + unkrxn4 C02 + 4 NADP + NADH 5.3. 3.5 Cholestenol dclta-isomerase ZYMST + SAM->ERGOST+SAH cdisoa Nucleotide Metabolism Histidine Blosynthesis YOL061W 2.7. 6. 1 PRS5 ribose-phosphate pyrophosphokinase R5P+ATP<->PRPP+AMP prs5 YBL068W 2.7. 6. 1 PRS4 ribose-phosphate pyrophosphokinase 4 R5P + ATP <-> PRPP + AMP prs4 YER099C 2.7. 6. 1 PRS2 ribose-phosphate pyrophosphokinase 2 R5P + ATP <-> PRPP + AMP prs2 YHLOIIC 2.7. 6. 1 PRS3 ribose-phosphatepyrophosphokinase3 R5P+ATP<->PRPP+AMP prs3 YKL181W 2.7. 6. 1 PRSI ribose-phosphatc pyrophosphokinase R5P + ATP <-> PRPP + AMP prsl YIR027C 3.5. 2.5 dall allantoinase ATN <-> ATT dall YIR029W 3.5. 3.4 dal2 allantoicase ATT <-> UGC + UREA dal2 YIR032C 3.5. 3. 19 dal3 ureidoglycolate hydrolase UGC <-> GLX + 2 NH3 + C02 dal3 Purine metabolism YJL005W 4. 6.1.1 CYR1 adenylate cyclase ATP->cAMP+PPI cyr1 YDR454C 2.7. 4.8 GUKI guanylate kinase GMP+ATP<->GDP+ADP gukI_1 YDR454C 2.7. 4.8 GUKI guanylatekinase DGMP+ATP<->DGDP+ADP gukl 2 YDR454C 2.7. 4.8 GUKI guanylate kinase GMP+DATP<->GDP+DADP gukI_3 YMR300C 2.4. 2.14 ade4 phosphonbosylpyrophosphate amidotransferase PRPP + GLN-> PPI + GLU + PRAM ade4 YGL234W 6.3. 4. 13 ade5,7 glycinamide ribotide synthetase and aminoimidazole PRAM+ATP+GLY<-> ADP+PI+GAR ade5 ribotide synthetase YDR408C 2. 1. 2.2 ade8 glycinamideribotidetransformylase GAR+FTHF->THF+FGAR ade8 YGR061C 6.3. 5. 3 ade6 5'-phosphoribosylformyl glycinamidine synthetase FGAR+ATP+GLN-> GLU+ADP+PI+FGAM ade6 YGL234W 6.3. 3.1 ade5, 7 Phosphoribosylformylglycinamidecyclo-ligase FGAM+ATP->ADP+PI+AIR ade7 YOR128C 4. 1. 1. 21 ade2 phosphoribosylamino-imidazole-carboxylase CAIR<->AIR+C02 ade2 YAR015W 6.3. 2.6 adel phosphoribosylaminoimidazolesuccinocarbozamide CAIR+ATP+ASP<->ADP+PI+SAICAR adel synthetase YLR359W 4.3. 2.2 ADE13 5'-Phosphoribosyl4-(N-succinocarboxamide)-5-SAICAR <-> FUM + AICAR adel3_1 aminoimidazole lyase YLR028C 2. 1. 2.3 ADE16 5-aminoimidazole-4-carboxamide ribonucleotide AICAR+FTHF<->THF+PRFICA adel6_1 (AICAR) transformylaseVIMP cyclohydrolase YMR120C 2. 1. 2.3 ADE17 5-aminoimidazole-4-carboxamide ribonucleotide AICAR+FTHF<->THF+PRFICA ade17_1 (AICAR) transformylaseVIMP cyclohydrolase YLR028C 3. 5. 4. 10 ADE16 5-aminoimidazole-4-carboxamide ribonucleotide PRFICA <-> IMP ade16_2 (AICAR) transformylaseVIMP cyclohydrolase YMR120C 2. 1. 2.3 ADE17 IMP cyclohydrolase PRFICA <-> IMP adel7 2 YNL220W 6.3. 4.4 ade12 adenylosuccinate synthetase IMP+GTP+ASP->GDP+PI+ASUC ade12 YLR359W 4.3. 2.2 ADE13 AdenylosuccinateLyase ASUC<->FUM+AMP adel3 2 YAR073W 1. 1. 1.205 fun63 putativeinosine-5'-monophosphatedehydrogenase IMP+NAD->NADH+XMP fun63 YHR216W 1. 1. 1. 205 pur5 purine excretion IMP+NAD->NADH+XMP pur5 YML056C 1. 1. 1. 205 IMD4 brobable inosine-5'-monophosphate dehydrogenase IMP+NAD-7gt;NADH+XMP prm5 (IMP YLR432W 1. 1. 1. 205 IMD3 probable inosine-5'-monophosphate dehydrogenese IMP+NAD-7gt;NADH+XMP prm4 (IMP YAR075W I.I.I. 205 YAR075 Protein with strong similarity to inosine-5'-IMP + NAD-> NADH + XMP prm6 W monophosphate dehydrogenase, frameshifted from YAR073W, possible pseudogene YMR217W 6.3. 5.2, GUAI GMP synthase XMP + ATP + GLN-> GLU + AMP + PPI + GMP gual 6.3. 4. 1 YML035C 3.5. 4.6 amdl AMP deaminase AMP->IMP+NH3 amdl YGL248W 3. 1. 4. 17 PDEI 3', 5'-Cyclic-nucleotide phosphodiesterase, low affinity cAMP-> AMP pdel YOR360C 3. 1. 4. 17 pde2 3', 5'-Cyclic-nucleotide phosphodiesterase, high affinity cAMP-> AMP pde2 1 YOR360C 3. 1. 4. 17 pde2 cdAMP ->DAMP pde2_2 YOR360C 3. 1. 4. 17 pde2 c ! MP->'MP pde23 YOR360C 3. 1. 4. 17 pde2 cGMP - > GMP pde2_4 YOR360C 3. 1. 4. 17 pde2 cCMP a CMP pde2_5 YDR530C 2.7. 7.53 APA2 5', 5'-P-1, P-4-tetraphoshate phosphorylase II ADP+ATP->PI+ATRP apa2 YCLOSOC 2.7. 7.53 apal 5', 5'-P-1, p-4-tetraphosphate phosphorylase II ADP+GTP->PI+ATRP apal_1 YCLOSOC 2.7. 7.53 apal 5', 5"-P-1,P-4-tetraphosphate phosphorylase II GDP+GTP->PI+GTRP apal_3 Pyrimidine metabollsm YJL130C 2. 1. 3.2 ura2 Aspartate-carbamoyltransferase CAP+ASP ->CAASP+PI ura2_1 YLR420W 3. 5. 2.3 ura4 dihydrooratase CAASP <-> DOROA una4 YKL216W 1. 3. 3. 1 ural dihydroorotate dehydrogenase DOROA + 02 <-> H202 + OROA ural I YKL216W 1. 3. 3. 1 PYRD Dihydroorotate dehydrogenase DOROA + Qm <-> QH2m + OROA ural 2 YML106W 2.4. 2. 10 URA5 Orotate phosphoribosyltransferase I OROA+PRPP<->PPI+OMP ura5 YMR271C 2.4. 2. 10 URA10 Orotate phosphoribosyltransferase 2 OROA+PRPP <->PPI+OMP ura10 YEL021W 4.1.1 23 ura3 orotidine-5'-phosphatedecarboxylase OMP-> C02 + UMP ura3 YKL024C 2.7. 4.14 URA6 Nucleoside-phosphate kinase ATP + UMP <-> ADP + UDP npk YHR128W 2.4. 2.9 furl UPRTase, Uracil phosphoribosyltransferase URA+PRPP->UMP+PPI furl YPR062W 3. 5. 4.1 FCYI cytosinedeaminase CYTS->URA+NH3 fcyl 2.7.1.21 Thymidine(deoxyuridine) kinase DU + ATP-> DUMP + ADP tdkl . 7.1. 21 Thymidine (deoxyuridine) kinase DT+ATP ->ADP+DTMP tdk2 YNR012W 2. 7. 1. 48 URKI Uridinekinase URI+GTP->UMP+GDP urkl_I YNR012W 2.7. 1.48 URKI Cytodine kinase CYTD+GTP->GDP+CMP urk1_2 YNR012W 2. 7. 1. 48 URKI Uridinekinase, convertsATPanduridinetoADPand URi+ATP->ADP+UMP urkl 3 UMP YLR209C 2.4. 2.4 PNPI Proteinwithsimilaritytohumanpurinenucleoside DU+PI<->URA+DRIP deoal phosphorylase, Thymidine (deoxyuridine) phosphorylase, Purine nucleotide phosphorylase YLR209C 2.4. 2.4 PNPI Protein with similarity to human purine nucleoside DT + Pl <-> THY + DRIP deoa2 phosphorylase, Thymidine (deoxyuridine) phosphorylase YLR245C 3.5. 4. 5 CDDI Cytidine deaminase CYTD-> URI + NH3 cddl I YLR245C 3. 5. 4.5 CDDI Cylidine deaminase DC->NH3+DU cdd1_2 YJR057W 2.7. 4.9 cdc8 dTMP kinase DTMP + ATP <-> ADP + DTDP cdc8 YDR353W 1. 6.4. 5 TRRI Thioredoxinreductase OTHIO + NADPH->NADP+RTHIO trrl YHRI06W 1. 6.4. 5 TRR2 mitochondrial thioredoxin reductase OTHIOm + NADPHm-> NADPm + RTHIOm trr2 YBR252W 3. 6. 1. 23 DUTI dUTPpyrophosphatase (dUTPase) DUTP->PPI+DUMP dutl YOR074C 2. 1. 1. 45 cdc21 Thymidylatesynthase DUMP+METTHF->DHF+DTMP cdc21 . 2. 7. 4. 14 Cytidylate kinase DCMP + ATP <-> ADP + DCDP cmkal . 2. 7. 4. 14 Cytidylate kinase CMP+ATP<->ADP+CDP cmka2 YHR144C 3.5. 4.12 DCDI dCMPdeaminase DCMP<->DUMP+NH3 dcdl YBL039C 6.3. 4.2 URA7 CTP synthase, highly homologus to URA8CTP UTP+GLN+ATP->GLU+CTP+ADP+PI synthase YJR103W 6.3. 4.2 URA8 CTP synthase UTP+GLN+ATP->GLU+CTP+ADP+PI ura8_1 YBL039C 6.3. 4.2 URA7 CTP synthase, highly homologus to URA8 CTP ATP+UTP+NH3->ADP+PI+CTP ura7_2 synthase YJR103W 6.3. 4.2 URA8 CTP synthase ATP+UTP+NHE->ADP+PI+CTP ura8_2 YNL292W 4. 2. 1. 70 PUS4 Preudouridine synthase URA+R5P<->PUR15P pus4 YPL212C 4.2. 1. 70 PUS1 intranuclear protein which exhibits a nucleotide-specific URA+R5P<->PUR15P pusl intron-dependent tRNA pscudouridine synthase activity YGL063W 4.2. 1. 70 PUS2 pscudouridine synthase 2 URA + R5P <-> PUR15P pus2 YEL001W 4. 2. 1. 70 degl Similar to rRNA methyltransferase (Caenorhabditis URA + RSP <-> PURI5P degl elegans) and hypothetical 28K protein (alkaline endoglucanase gene 5'region) from Bacillus sp.

Salvage Pathways YML022W 2.4. 2.7 APTI Adeninephosphoribosyltransferase AD++ PRPP-> PPI +AMP aptl YDR441C 2.4. 2.7 APT2 similar to adenine phosphoribosyltransferase AD + PRPP-> PPI + AMP apt2 YNL141W 3.5. 4.4 AAH 1 adenine amiohydrolase (adenine deaminase) ADN->INS+NH3 aah1a YNL141W 3.5. 4.4 AAHI adenineaminohydrolase (adeninedeaminase) DA->DIN+NH3 aahlb YLR209C 2.4. 2,1 PNP1 Purine nucleotide phosphorylase, Xanthosine DIN+PI<->HYXN+DRIP xapa1 phosphorylase YLR209C 2.4. 2. 1 PNPI Xanthosine phosphorylase, Purine nucleotide DA + pI <-> AD + DRIP xapa2 phosphorylase YLR209C 2.4. 2. 1 PNP1 Xanthosine phosphorylase DG+PI<->GN+DRIP xapa3 YLR209C 2.4. 2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide HYXN+RIP<->INS+PI xapa4 phosphorylase YLR209C 2.4. 2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide AD+RIP<->PI+ADN xapa5 phosphorylase YLR209C 2.4. 2.1 PNP1 Xanthosine phosphorylase, Purine nucleotide GN+RIP<->PI+GSN xapa6 phosphorylase YLR209C 2.4. 2. 1 PNP1 Xanthosine phosphorylase, Purine nucleotide XAN+RIP<->PI+XTSINE xapa7 phosphorylase YJR133W 2.4. 2.22 XPTI Xanthine-guaninephosphoribosyltransferase XAN+PRPP->XMP+PPI gptl YDR400W 3.2. 2. 1 urhl Purine nucleosidase GSN-> GN + RIB pur2l YDR400W 3.2. 2. 1 urhl Purinenucleosidase ADN->AD+RIB purl I YJR) 05W 2. 7. 1. 20 YJR105 Adenosine kinase ADN+ATP->AMP+ADP prm2 W YDR226W 2.7. 4.3 adk1 cytosolic adenlate kinase ATP+AMP<->2 ADP adkl_1 YDR226W 2.7. 4.3 adk1 cytosolic adenylate kinase GTP+AMP<->DP+GDP alk1_2 YDR226W 2.7. 4.3 adkl cytosolic adenylate kinase ITP + AMP <-> ADP + IDP adkl 3 YER170W 2.7. 4.3 ADK2 Adenylate kinase (mitochondrial GTP : AMP ATPm+AMPm<->2 ADPm adk2_1 phosphotransferase) YER170W 2.7. 4.3 adk2 Adenylate kinase (mitochondrial GTP : AMP GTPm+AMPm<->ADPm+GDPm adk22 phosphotransferase) YER170W 2.7. 4.3 adk2 Adenylate kinase (mitochondrial GTP: AMP ITPm + AMPm <-> ADPm + IDPm adk2 3 phosphotransferase) YGR180C 1. 17.4. 1 RNR4 ribonucleotide reductase, small subunit (alt), beta chain YIL066C 1. 17. 4. 1 RNR3 Ribonucleotide reductase (ribonucleoside-diphosphate ADP + RTHIO-> DADP + OTHIO mr3 reductase) large subunit, alpha chain YJL026W 1. 17. 4. 1 mr2 small subunit of ribonucleotide reductase, beta chain YKL067W 2.7. 4.6 YNKI Nucleoside-diphosphatekinase UDP+ATP<->UTP+ADP ynkl_I YKL067W 2.7. 4.6 YNKI Nucleoside-diphosphate kinase CDP+ATP<->CTP+ADP ynk1_2 YKL067W 2.7. 4.6 YNKI Nucleoside-diphsophate kinase DGDP+ATP<->DGTP+ADP ynk1_3 YKL067W 2.7. 4.6 YNKI Nucleoside-diphosphate kinase DUDP+ATP<->DUTP+ADP yn1_4 YKL067W 2.7. 4.6 YNKI Nuclcoside-diphosphate kinase DCDP + ATP <-> DCTP + ADP ynkl 5 YKL067W 2.7. 4.6 YNK1 Nucleoside-diphosphate kinase DTDP+ATP<->DTTP+ADP yn1_6 YKL067W 2.7. 4.6 YNK1 Nucleoside-diphosphate kinase DADP+ATP<->DATP+ADP yn1_7 YKL067W 2.7. 4.6 YNKI Nucleoside diphosphate kinase GDP + ATP <-> GTP + ADP ynkl 8 YKL067W 2.7. 4.6 YNKI Nucleosidediphosphatekinase IDP+pTP<->ITP+IDP ynkl_9 2. 7. 4. 11 Adenylate kinase, dAMP kinase DAMP + ATP <-> DADP + ADP dampk YNL141W 3.5. 4.2 AAHI Adeninedeaminase AD->NH3+HYXN yicp 2.7.1.73 Inosine kinase INS+ATP->IMP+ADP gsk1 2.7.1.73 Guanosine kinase GSN+ATP->GMP+ADP gsk2 YDR399W 2.4. 2.8 HPTI Hypoxanthine phosphoribosyltransferase HYXN+PRPP->PPI+IMP hpt1_1 YDR399W 2.4. 2.8 HPTI Hypoxanthine phosphoribosyltransferase GN+PRPP ->PPI+GMP hpt1_2 . 2. 4.2. 3 Uridine phosphorylase URI+PI<->URA+RIP udp YKL024C 2.1.4 URA6 Uridylate kinase UMP+ATP<->UDP+ADP pyrh1 YKL024C 2. 1. 4.-URA6 Undylue kinase DUMP + ATP <-> DUDP + ADP pyrh2 .3. 2. 2. 10 CMPglycosylase CMP->CYTS+R5P cmpg YHR144C 3. 5. 4. 13 DCDI dCTPdeaminase DCTP->DUTP+NH3 dcd . 3.1. 3.5 5'-Nucleotidase DUMP->DU+Pt usha) . 3.1. 3.5 5'-Nucleotidase DTMP-> DT + pI usha2 . 3.1. 3.5 5'-Nucleotidase DAMP-> DA + PI usha3 . 3.1. 3.5 5'-Nucleotidase DGMP-> DG + PI usha4 . 3.1. 3.5 5'-Nucleotidase DCMP->DC+pl usha5 . 3.1. 3. 5 5'-Nucleotidase CMP->CYTD+PI usha6 . 3.1. 3. 5 5'-Nucleotidase AMP->P) +ADN usha7 . 3.1. 3.5 5'-Nucleotidase GMP->PI+GSN usha8 . 3.1. 3.5 5'-Nucleotidase IMP-> PI + INS usha9 . 3.1.1 3.5 5'-Nucleotidase XMP->PI+XTSINE ushal2 . 3.1.3. 5 5'-Nucleotidase UMP->PI+URI ushall YER070W .117.4.1 RNR Ribonucleoside-diphosphate reductase ADP+RTHIO->DADP+OTHIO mrl_1 YER070W 1. 17. 4. 1 RNRI Ribonucleoside-diphosphatereductase GDP+RTHIO->DGDP+OTHIO mrl 2 YER070W 1.17.4.1 RNR1 Ribonucleoside-diphosphate reductase CDP+RTHIO->DCDP+OTHO mr1_3 YER070W 1.17.4.1 RNR1 Ribonucleoside-diphosphate reductase UDP+RTHIO->OTHIO+DUDP mrl_4 . 1.17 4.2 Ribonucleoside-triphosphate reductase ATP + RTHIO-> DATP + OTHIO nrddl - 1. 17. 4.2 Ribonucleoside-triphosphate reductase GTP+RTHIO->DGTP+OTHIO nrdd2 1. 17. 4.2 Ribonucleoside-triphosphate reductase CTP + RTHIO-> DCTP + OTHIO nrdd3 1. 17. 4.2 Ribonucleoside-triphosphate reducase UTP+RTHIO>OTHIO+DUTP nrdd4 3. 6. 1. Nucleoside triphosphatase GTP>GSN+ 3 PI mult1 3. 6.1. Nucleoside triphosphatase DGTP->DG+3 PI mutt2 YML035C 3.2. 2.4 AMDI AMP deaminase AMP AD + R5P amn YBR284W 3.2. 2.4 YBR284 Protein with similarity to AMP deaminase AMP-> AD + R5P amnl W YJL070C 3.2. 2.4 YJL070C Protein with similarity to AMP deaminase AMP-> AD + R5P amn2 Amino Acid Metabollsm Glutamate Metabollsm (Aminosugars met) YMR2SOW 4. 1. 1. 15 GADI GlutamatedecarboxylaseB GLU->GABA+C02 btn2 YGR019W 2. 6.1.19 ugal Aminobutyrate aminoransaminase2 GABA+AKG->SUCCSAL+GLU ugal YBR006w 1.2.1.16 YBR06 Succinate semiaklehyde dehydrogenase-NADP SUCCSAL+NADP->SUCC+NADPH gabda w YKL104C 2. 6. 1.16 GEA1 GLutamine_fructose-6-phosphate amidotransferase F6P+GLN->GLU+GA6P gfal (glucoseamine-6-phosphate synthase) YFL017C 2. 3. 1. 4 GNA1 Glucosamine-phosphateN-acetyltransferase ACCOA+GA6P<->COA+NAGA6P gnal YELOS8W 5. 4.2. 3 PCMI PhosphoacetylglucosamineMutase NAGAIP<->NAGA6P pcmla YDL103C 2.7. 7.23 QRII N-Acetylglucosamine-1-phosphate-uridyltransferase UTP+NAGAIP <-> UDPNAG+PPI qril YBR023C 2.4. 1. 16 chs3 chitin synthase 3 UDPNAG-> CHIT + UDP chs3 YBR038W 2. 4. 1. 16 CHS2 chitin synthase 2 UDPNAG-> CHIT + UDP chs2 YNL192W 2. 4. 1. 16 CHSI chitin synthase 2 UDPNAG-> CHIT + UDP chsl YHR037W 1. 5. 1. 12 put2 delta-1-pyrroline-5-carboxylatedehydrogenase GLUGSALm+NADPm->NADPHm+GLUm put2_1 PSCm + NADm-> NADHm + GLUm put2 YDL171C 1.4.1.14 GLT1 Gutamate synthese (NADH) AKG+GLN+NADH->NAD+2 GLU gtl YDL21 SC 1. 4. 1. 4 GDH2 glutamate dehydrogenase GLU + NAD-> AKG + NH3 + NADH gdh2 YAL062W 1. 4. 1. 4 GDH3 NADP-linkedglutamatedehydrogenase AKG+NH3+NADPH<->GLU+NADP gdh3 YOR375C 1. 4. 1. 4 GDHI NADP-specificglutamatedehydrogenase AKG+NH3+NADPH<->GLU+NADP gdhl YPR03SW 6.3. 1.2 gin l glutamine synthetase GLU + NH3 + ATP-> GLN + ADP + PI glnl YEL058W 5. 4.2. 3 PCMI Phosphoglucosaminemutase GA6P<->GAIP pcmlb . 3.1.5. 2 GlutaminaseA GLN->GLU+NH3 glnasea . 3. 5.1. 2 GlutaminaseB GLN->GLU+NH3 glnaseb Glucosamine -5. 3. 1. 10 Glucosamine-6-phosphatedeaminase GA6P->F6P+NH3 nagb Arab ! nose YBR149W 1.1.1.117 ARA1 D-arabinose 1-dehydrogenase (NAD (P) +). ARAB+NAD->ARABLAC+NADH aral I YBR149W 1.1.1.117 ARA1 D-arabinose 1-dehydrogenase (NAD (P) +). ARAB+NADP->ARABLAC+NADPH aral_2 Xylose YGR194C 2. 7.1.17 XKS1 Xylukoinase XUL+ATP->X5P+ADP xks1 Mannitol 1.1.1.17 Mannitol-1-phosphate 5-dehydrogenase MNT-6P+NAD<->F6P+NADH mtld Alanine and Aspartat Metabolism YKL106W 2. 6.1.1 AAT1 Asparate transaminase OAm+GLUm<->ASPm+AKGm aatl_1 YLR027C 2. 6. 1. 1 AAT2 Asparate transminase OA+GLU<->ASP+AKG aat2_1 YAR035W 2. 3. 1. 7 YATI Camitine O-acetyltransferase COAm_ACARm->ACCOAm+CARM yat1 YML042W 2. 3. 1. 7 CAT2 Camitine transferase ACCOA+CAR>COA+ACAR cat2 YDR111C 2. 6. 1. 2 YDR111 putativealaninetransaminase PYR+GLU<->AKG+ALA alab C YLR089C 2. 6. 1. 2 YLR089 alanine aminotnansferase, mitochondrial precursor PYRm + GLUm <-> AKGm + ALAm cfx2 C (glutamic-- YPR145W 6.3. 5.4 ASN1 asparagine synthetase ASP+ATP+GLN->GLU+ASN+AMP+PPI asn1 YGR124W 6.3. 5.4 ASN2 asparagine synthetase ASPASP+ATP+GLN->GLU+ASN+AMP+PPI asn2 YLL062C 2.1.1.10 MHT1 Putative cobalamin-dependent homocysteine S- SAM+HCYS->SAH+MET mht1 methyltransferase, Homocysteine S-methyltransferase YPL273W 2. 1. 1. 10 SAM4 Putative cobalamin-dependent homocysteine S- SAM+HCYS->SAH+MT sam4 methyltransferase Asparagine YCR024c 6. 1. 1. 22 YCR024c asn-tRNA synthetase, mitochondrial ATPm+aSPm+TRNAm->AMPm+PPIm+ mas ASPTRNAm YHR019C 6. 1. 1. 23 DED81 asn-tRNA synthetase ATP+ASP+TRNA->AMP+PPI+ASPTRNA ded81 YLR155C 3. 5. 1. 1 ASP3-1 Asparaginase, extracellular ASN->ASP+NH3 asp3-1 YLR157C 3. 5. 1. 1 ASP3-2 Asparaginase, extracellular ASN->ASP+NH3 asp3 2 YLR158C 3. 5. 1. 1 ASP3-3 Asparaginase, extracellular ASN->ASP+NH3 asp3 3 YLR160C 3. 5. 1. 1 ASP3-4 Asparaginase, extracellular ASN->ASP+NH3 asp3_4 YDR321W 3. 5.1.1 asp1 Asparaginase ASN->ASP+NH3 asp1 Glycine, serine and threonine metabolism YER081W 1.1.1.95 ser3 Phospoglycentate dehydrogenase JPG+NAD->NADH+PHP ser3 YIL074C 1. 1. 1. 95 seß3 Phosphoglyceratedehydrogenase 3PG+NAD->NADH+PHP ser33 YOR184W 2.6. 1.52 ser1 phosphoserine transaminase PHP+GLU->AKG+3PSEK ser1_1 YGR208W 3. 1. 3.3 ser2 phosphoserinephosphatase 3PSER->PI+SER ser2 YBR263W 2. 1. 2. 1 SHMI Glycinehydroxymethyltransferase THFm+SERm<->GLYm+METTHFm shml YLR058C 2. 1. 2. 1 SHM2 Glycine hydroxymethyltransferase THF + SER <-> GLY + METTHF shm2 YFL030W 2.6. 1. 44 YFL030 Putative alanine glyoxylae aminotransferase (serine ALA+GLX<->PYR+GLY agt W pyruvate aminotransferase) <BR> YDR019C 2. 1. 2. 10 GCVI glycine cleavage T protein (T subunit of glycine GLYm + THFm + NADm-> METTHFm + NADHm gcvl decarboxylase complex + C02 + NH3 YDR019C 2. 1. 2. 10 GCVI glycine cleavage T protein (T subunit of glycine GLY + THF + NAD-> METTHF + NADH + C02 + gcvl 2 decarboxylase complex NH3 YER052C 2.7. 2.4 hom3 Aspartake kinase, Aspartate kinase I, II, III ASP+ATP->ADP+BASP hom3 YDR158W 1. 2. 1. 11 hom2 aspartic semi-aldehyde dehydrogenase. Aspartats BASP+NADPH->NADP+PI+ASPSA hom2 semialdehyde dehydrogenase YJR139C 1. 1. 1. 3 hom6 Homoserinedehydrogenasel ASPSA+NADH->NAD+HSER hom6 1 YJR139C 1. 1. 1. 3 hom6 Homoserine dehydrogenase I ASPSA+NADPH->NADP+HSER hom6_2 YHR025W 2.7. 1. 39 thrl homoserine kinase HSER+ATP->ATP+PHSER thr1 YCR053W 4.2. 99.2 thr4 threonine synthase PHSER->PI+THR thr4_1 YGR115W 4.2. 1. 22 CYS4 Cystathioninebeta-synthase SER+HCYS->LLCT cys4 YEL046C 4. 1. 2. 5 GLYI Threonine Aldolase GLY + ACAL-> THR glyl YMR189W 1. 4.4. 2 GCV2 Glycine decarboxylase complex (P-subunit), glycine GLYm + LlPOm <-> SAPm + C02m gcv2 synthase (P-subunit), Glycine cleavage system (P- subunit) YCL064C 4. 2. 1. 16 chal threoninedeaminase THR->NH3+OBUT chal I YER086W 4.2. 1. 16 ilvl L-Serinedehydratase THRm->NH3m+OBUTm ilvl YCL064C 4.2. 1. 13 chal catabolic serine (threonine) dehydratase SER->PYR+NH3 chat2 YIL167W 4. 2. 1. 13 YIL 167 catabolic serine (threonine) dehydratase SER->PYR+NH3 sdl1 W 1.1.1.103 Threonine dehydrogenase THR+NAD->GLY+AC+NADH table Methlonine metabolism YFR055W 4. 4. 1. 8 YFR055 Cystathionine-b-lyase LLCT->HCYS+PYR+NH3 metc W YER043C 3.3. 1. 1 SAHI putativeS-adenosyl-L-homocysteinehydrolase SAH->HCYS+ADN sahl YER091C 2.1.1.14 met6 vitamin B12-(cobalamin)-independent isozyme of HCYS+MTHPTGLY ->THPGLU+MET met6 methionine synthase (also called N5- methyltetrahydrofolate homocysteine methyltransferase or 5-methyltetrahydropteroyl triglutamate homocysteine methyltransferase) -2. 1. 1. 13 Methionine synthase HCYS+MTHF->THF+MET met62 YAL012W 4. 4. 1. 1 cys3 cystathionine gamma-lyase LLCT-> CYS + NH3 + OBUT cys3 YNL277W 2. 3. 1. 31 met2 homoserine O-trans-acetylase ACCOA + HSER <-> COA + OAHSER met2 YLR303W 4.2. 99.10 MET17 O-Acetylhomoserine (thiol)-lyase OAHSER+METH->MET+AC metl7 1 YLR303W 4. 2. 99.8 MET17 O-Acetylhomoserine (thiol)-lyase OAHSER+H2S->AC+HCYS met17_2 YLR303W 4.2. 99. 8, metl7 O-acetylhomoserinesulfhydrylase (OAHSHLase) ; OAHSER+H2S->AC+HCYS met17_3 4.2. 99. 10 converts O-acetlhomoserine into homocysteine YML082W 4.2. 99.9 YML082 putative cystathionine gamma-synthase OSLHSER <-> SUCC + OBUT + NH4 metl7h W YDR502C 2.5. 1.6 sam2 S-adenosylmethionine synthetase MET+ATP->PPI+PI+SAM sam2 YLRI80W 2. 5. 1. 6 saml S-adenosylmethioninesynthetase MET+ATP->PPI+PI+SAM saml YLR 172C 2. 1. 1. 98 DPH5 Diphthine synthase SAM + CALH-> SAH + DPTH dph5 Cysteine Biosynthesis YJROIOW 2.7. 7.4 met3 ATP sulfurylase SLF+ATP->PPI+APS met3 YKL001C 2. 7. 1. 25 metl4 adenylylsulfatekinase APS++ ATP->ADP+PAPS metl4 YFR030W 1. 8. 1. 2 metl0 sulfite reductase H2SO3+3NADPH<->H25+3NADP met10 2.3.1.30 serine transacteylase SER+ACCOA->COA+ASER cysl YGR012W 4.2. 99.8 YGR012 putative cysteine synthase (O-acetylserine ASER+H2S->AC+CYS sul11 W sulfhydrylase) (O- YOL064C 3. 1. 3. 7 MET22 3'-5'Bisphosphate nuelcotidase PAP-> AMP + Pl met22 TPR167C 1.8. 99.4 MET16 PAPSReductase PAPS+RTHIO->OTHIO + H2S03++ P metl6 YCLOMC 2.7. 7.5 apal diadenosine 5',5"-P1,P4-tetraphosphate phosphorylase I ADP+SLF<->PI+APS apa1_2 Branched Chain Amino Acid Metabolism (Valine, Leucine and Isoleucine) YHR208W 2. 6. 1. 42 BATI Branched chain amino acid aminotransferase OICAPm+GLUm<->AKGm+LEUm bat1_1 YHR208W 2.6. 1.42 BATI Branched chain amino acid aminotransferase OMYALm+GLUm<->AKGm+ILEm bat1_2 YJR148W 2. 6. 1. 42 BAT2 branched-chain amino said transaminase, highly similar OMVAL+GLU<->AKG+ILE bat2_1 to mammalian ECA39, which is regulated by the oncogene myc YJR148W 2. 6. 1. 42 BAT2 Branched chain amino acid aminotransferase OTVAL+GLU<->AKG+VAL bat2_2 YJR148W 2.6. 1. 42 BAT2 branched-chain amino acid transaminase, highly similar OICAP+GLU<->AKG+LEU bat2_3 to mammalian ECA39, which is regulated by the oncogene myc YMR108W 4. 1. 3. 18 ilv2 Acetolactate synthase, large subunit OBUTm+PYRm->ABUTm+CO2m ilv2_1 YCL009C 4. 1. 3. 18 ILV6 Acetolactate synthase, small subunit YMR108W 4.1.3.18 ilv2 Acetolactate synthase, large subunit 2PYRm->CO2m+ACLACm ilv2_2 YCL009C 4. 1. 3. 18 ILV6 Acetolactate synthase, small subunit YLR355C 1. 1. 1. 86 ilv5 Keto-acidreductoisomerase ACLACm+NADPHm->NADPm+DHVALm ilv5_l YLR355C 1. 1. 1. 86 ilv5 Keto-acidreductoisomerase ABUTm+NADPHm-oNADPm+DHMVAm ilv5 2 YJR016C 4. 2. 1. 9 ilv3 Dihydroxy acid dehydratase DHVALm->OTVALm ilv3_1 YJR016C 4. 2. 1. 9 ilv3 Dihydroxyaciddehydratase DHMVAm->OMVALm ilv3 2 YNL104C 4. 1. 3. 12 LEU4 alpha-isopropylmalate synthase (2-Isopropylmalate ACCOAm + OIVALm-> COAm + IPPMALm leu4 Synthase) YGL009C 4. 2. 1. 33 leul Isopropylmalateisomerase CBHCAP <-> IPPMAL leul_I YGL009C 4. 2. 1. 33 leul isopropylmalate isomerase PPMAL <-> IPPMAL leul 2 YCLj018W 1.1.1.85 leu2 beta-IPM (isopropylamate)dehydrogenase IPPMAL+NAD->NADH+OICAP+CO2 leu2 Lysine biosynthesis/degradation 4. 2. 1. 79 2-Methylcitrate dehydratase HCITm <-> HACNm lys3 YDR234W 4. 2. 1. 36 lys4 Homoaconitate hydratase HICITm <-> HACNm lys4 YIL094C 1.1.1.155 LYS12 homoiscoitrate dehydrogense (Strathem: 1. 1. 1. 87) HICITm+NADm<->OXAm+C02m+NADHm lysl2 non-enzymatic OXAm<->CO2M+AKAm lys12b . 2.6.1. 39 2-Aminoadipate transaminase AKA + GLU <-> AMA + AKG amit YBR115C 1.2.1.31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA+NADPH+ATP->AMASA+NADP+AMP lys2_1 subunit + PPI YGL154C 1. 2. 1.31 lys5 L-Aminoadipate-temlaldehyde dehydrogenase, small subunit YBRI 15C 1. 2. 1. 31 lys2 L-Aminoadipate-semialdehyde dehydrogenase, large AMA+NADPH+ATP->AMASA+NADP+AMP lys2_2 subunit PPI YGLI54C 1. 2. 1. 31 lys5 L-Aminoadipate-semialdehyde dehydrogenase, small subunit YNROSOC 1.5.1.10 lys9 Saccaropine dehydrogenase (NADP+, L-glutamate GLU + AMASA + NADPH <-> SACP + NADP lys9 forming) YIR034C 1. 5. 1. 7 lysl Saccharopinedehydrogenase (NAD+, L-lysineforming) SACP+NAD <->LYS+AKG+NADH lysla YDR037W 6. 1. 1. 6 krsl lysyl-tRNAsynthetase, cytosolic ATP+LYS+LTRNA->AMP+PPI+LLTRNA krsl YNL073W 6. 1. 1. 6 msk1 lysyl-tRNA synthetase, mitochondial ATPm+LYSm+LTRNAm->AMPm+PPIm+ msk1 LLTRNAm YDR368W 1.1.1. YPR1 similar to aldo-keto reductase Arginine metabolism YMR062C 2. 3. 1. 1 ECM40 Amino-acid-N-acetltransferase GLUm+ACCOAm->COAm+NAGLM cem40_1 YER069W 2.7. 2.8 arg5 Acetylglutamate kinase NAGLUm + ATPm-> ADPm + NAGLUPm arg6 YER069W 1. 2. 1. 38 arg5 N-acetyl-gamma-glutamyl-phosphate reductase and NAGLUPm + NADPHm-> NADPm + Plm + arg5 acetylglutamate kinase NAGLUSm YOL140W 2. 6. 1. 11 arg8 Acetylomithine aminotransferase NAGLUSm+GLUm->AKGm+NAKORNm arg8 YMR062C 2.3. 1. 35 ECM40 GlutamateN-acetyltransferase NAORNm+GLUm->ORNm+NAGLUm ecm40_2 YJL130C 6.3. 5.5 ura2 carbamoyi-phophate synthetase, aspartate GLN + 2 ATP + C02-> GLU + CAP + 2 ADP + Pi ura2_2 transcarbamylase, and glutamine amidotransferase YJR) 09C 6.3. 5. 5 CPA2 carbamyl phosphate synthetase, large chain GLN + 2 ATP+C02->GLU +CAP +2ADP +Pl cpa2 YOR303W 6.3. 5.5 cpal Carbamoyl phosphate synthetase, samit chain, arginine specific YJL088W 2. 1. 3.3 arg3 Omithine carbamoyltransferase ORN + CAP-> CITR + pl arg3 YLR438W 2. 6. 1. 13 car2 Omithine transaminase ORN+AKG->GLUGSAL+GLU car2 YOL058W 6.3. 4.5 arg1 arginosuccinate synthetase CITR+ASP+ATP<->AMP+PPI+ARGSUCC arg1 YHRO) 8C 4.3. 2. 1 arg4 argininosuccinatelyase ARGSUCC<->FUM+ARG arg4 YKL184W 4.1.17 spe1 Omithine decarboxylase ORN->PTRSC+CO2 spe1 YOL052C 4. 1. 1. 50 spe2 S-adenosylmethionine decarboxylase SAM <-> DSAM + C02 spe2 YPR069C 2. 5. 1. 16 SPE3 putrescine transferase (spermidine PTRSC+SAM->SPRMD+5MTA spe3 synthase) YLR146C 2. 5. 1. 22 SPE4 Spermine synthase DSAM + SPRMD->5MTA + SPRM spe4 YDR242W 3. 5. 1. 4 AMD2 Amidase GBAD->GBAT+NH3 amd2t YMR293C 3. 5. 1. 4 YMR293 Probable Amidase GBAD->GBAT+NH3 amd C YPLIIIW 3.5. 3.1 carl arginase ARG->ORN+UREA car1 YDR341C 6.1.1.19 YDR341 arginyl-tRNA synthetase ATP+ART+ATRNA->AMP+PPI+ALTRNA atma C YHR091C 6.1.19 MSR1 arginyl-tRNA synthetase ATP+ART+ATRNA->AMP+PPI+ALTRNA msr1 YHR068W 1. 5.99. 6 DYSI deoxyhypusine synthase SPRMD + Qm-> DAPRP + QH2m dysl Histidine metabollsm YER055C 2.4. 2. 17 hisl ATPphosphoribosyltmnsferase PRPP+ATP->PPI + PRBATP hisl YCL030C 3.6. 1. 31 his4 phosphoribosyl-AMP cyclohydrolase/phosphoribosyl- PRBATP->PPI+PRBAMP his4_1 ATP pyrophosphohydrolase/histidinol dehydrogenase YCL030C 3.5. 4. 19 his4 histidinoldehydrogenase PRBAMP->PRFP his4 2 YIL020C 5. 3. 1. 16 his6 phosphoribosyl-5-amino-1-phospharibosyl-4-PRFP->PRLP his6 imidazolecarboxiamide isomerase YOR202W 4.2. 1. 19 his3 imidazoleglycerol-phosphate dehydratase DIMGP-> IMACP his3 YILI 16W 2. 6. 1. 9 his5 histidinol-phosphate aminotransferase IMACP+GLU->AKG+HISOLP his5 YFR025C 3. 1. 3. 15 his2 Histidinolphosphatase HISOLP->PI+HISOL his2 YCL030C 1. 1. 1. 23 his4 phosphoribosyl-AMP cyclohydrolase/phosphoribosyl-HISOL+2NAD-> HIS+2NADH his4 3 ATP pyrophosphohydrolase/histidinol dehydrogenase YBR248C 2.4. 2- his7 glutamine amidotransferase : cyclase PRLP+GLP->GLU+AICAR+DIMGP his7 YPR033C 6. 1.1.21 hts1 histidyl-tRNA synthetase ATP+HIS+HTRNA->AMP+PPI+HHTRNA hts1 YBR034C 2.1.1. hmt1 bnRNP arginine N-methyltransferase SAM+HIS->SAH+MHIS hmt1 YCL054W 2.1.1. spb1 putative RNA methyltransferase YMLI 10C 2.1.1. coq5 ubiquinone biosynthesis methlytransferase COQ5 YOR201C 2. 1. 1.-pet56 rRNA (guanosine-2'-O-)-methyltransferase YPL266W 2. 1. 1.-diml dimshyladenosinetransferase Phenylatanine, tyrosine and tryptophan blosynthesis (Aromatic Amino Acids) YBR249C 4. 1. 2. 15 AR04 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) E4P+PEP->P ! +3DDAH7P aro4 synthase isoenzyme YDR035W 4. 1. 2. 15 AR03 DAHP synthase\ ; a. k. a. phospho-2-dehydro-3- E4P+PEP->PI+3DDAH7P aro3 deoxyheptonate aldolase, phenylalanine-inhibitef/ ; phospho-2-keto-3-deoxyheptonate nldolase\ ; 2-dehydro- 3-deoxyphosphoheptonste aldolase; 3-deox-D- arabine-heptulosonate-7-phosphate synthase YDR127W 4. 6. 1. 3 aml pentafunctionalarompolypeptide (contains: 3- 3DDAH7P->DQT+PI aro1_1 dehydroquinate synthase, 3-dehydroquinate dehydratase (3-dehydroquinase), shikimate 5-dehydrogenase, shikimate kinase, and epsp synthase) YDR127W 4. 2. 1. 10 aro I 3-Dehydroquinate dehydratase DQT->DHSK aro 1-2 YDR127W 1. 1. 1. 25 arol Shikimatedehydrogenase DHSK+NADPH->SME+NADP arol 3 YDR127W 2. 7.1.71 aro1 Shikimate kinase 1, 11 SME + ATP-> ADP + SME5P arol 4 YDR127W 2. 5.1.19 aro1 3-Phosphoshikimate-1-catoxyvinyltransferase SME5P+PEP->JPSME+PI aro1_5 YGL148W 4. 6. 1. 4 aro2 Chorismatesynthase 3PSME->PI+CHOR aro2 YPR060C 5.4. 99. 5 aro7 Chorismare mutase CHOR->PHEN aro7 YNL316C 4. 2. 1. 51 pha2 prephenatedehydratase PHEN->CO2+PHPYR pha2 YHR137W 2. 6.1 ARO9 putative aromatic amino acid aminotransferase II PHPYR+GLU<->AKG+PHE aro9_1 YBR166C 1. 3. 1. 13 tyrl Prephenatedehydrogenase (NADP+) PHEN+NADP->4HPP+C02+NADPH tyrl YGL202W 2. 6.1. ARO8 aromatic amino acid aminotransferase I 4HPP + GLU-> AKG + TYR aro8 THR137W 2. 6.1.- ARO9 aromatic amino acid aminotransferase 11 4HPP+GLU->AKG+TYR aro9_2 1.3.1.12 Prephanate dehydrogenase PHEN+NAD->4HPP+CO2+NADH tyr2 YER090W 4. 1. 3.27 trp2 Anthranilate synthase CHOR+GLN->GLU+PYR+AN trp2_1 YKL211C 4. 1. 3.27 trp3 Anthranilatesynthase CHOR+GLN->GLU+PYR+AN trp3_1 YDR354W 2.4. 2. 18 trp4 anthranilatephosphoribosyl transferase AN+PRPP-> PPI + NPRAN trp4 YDR007W 5. 3. 1. 24 trpl n- (5'-phosphoribosyl)-anthranilateisomerase NPRAN->CPAD5P trpl YKL211C 4.1.1. 48 trp3 lndoleglycerolphosphate synthase CPADSP->C02+IGPtrp3 2 YGL026C 4. 2. 1. 20 trp5 trptophan synthetase IGP+SER->T3PI+TRP trp5 YDR256C 1. 11. 1. 6 CTAI catalase A 2 H202-> 02 ctal YGR088W 1.11.1. 6 CTTI cytoplasmic catalase T 2 H202-> 02 cttl YKL106W 2. 6.1.1 AAT1 Asparate aminotransferase 4HPP+GLU<->ATG+TYR aat1_2 YLR027C 2. 6. 1. 1 AAT2 Asparateaminotransferase 4HPP+GLU<->AKG+TYR aat22 YMR170C 1. 2. 1. 5 ALD2 Cytosolic aldeyhde dehydrogenase ACAL + NAD-> NADH + AC ald2 YMR169C 1. 2. 1. 5 ALD3 strong similarity to aldehyde dehydrogenase ACAL + NAD-> NADH + AC ald3 YOR374W 1. 2. 1. 3 ALD4 mitochondrial aldehyde dehydrogense ACALm+NADM->NADHm+ACm ald4_1 YOR374W 1. 2. 1. 3 ALD4 mitochondrial aldehyde dehydrogenase ACALm + NADPm-> NADPHm + ACm ald4_2 YER073W 1. 2. 1. 3 ALD5 mitochondrial Aldehyde Dehydrogenase ACALm + NADPm-> NADPHm + ACm ald5 1 YPL061W 1.2.1. 3 ALD6 CytosolicAldehydeDehydrogenase ACAL+NADP-'NADPH+AC ald6 YJR078W 1. 13. 11. 1 YJR078 Protein with similarity to indoleamine 2, 3- TRP+O2->FKYN tdo2 W dioxygenases, which catalyze conversion of tryptophan and other indole derivatives into kynurenines, Tryptophan 2, 3-dioxygenase -3. 5. 1. 9 Kynurenine fommamidase FKYN-> FOR + KYN kfor YLR231C 3. 7. 1. 3 YLR231 probablekynureninase (L-kynureninchydrolase) KYN->ALA+AN kynu_l C YBL098W 1. 14. 13. 9 YBL098 Kynurenine 3-hydroxylase, NADPH-dependent flavin KYN + NADPH + 02-> HKYN + NADP kmo W monooxygenase that catalyzes the hydroxylation of kynurenine to 3-hydroxykynurenine in tryptophan degradation and nicotinic acid synthesis, Kynurenine 3- monooxygenase YLR23) C 3. 7. 1. 3 YLR231 probablekynureninase (L-kynureninchydrolase) HKYN->HAN+ALA kynu 2 C YJR025C 1. 13. 11. 6 BNA1 3-hydroxyanthranilate 3, 4-dioxygenase (3-HAO) (3-HAN + 02-> CMUSA bnal hydroxyanthranilic acid dioxygenase) (3- hydroxyanthranilatehydroxyanthranilic acid dioxygenase) (3-hydroxyanthranilate oxygenase) 4.1.1.45 Picolinic acid decarboxylase CMUSA->CO2+AM6SA ssan -1. 2. 1. 32 AM6SA + NAD-> AMUCO + NADH aaab 1. 5. 5.1. AMUCO+NADPH->AKA+NADP+NH4 saac 1.3.11. 27 4-Hydroxyphenylpyruvate dioxygenase 4HPP + 02-> HOMOGEN + C02 tyrdega 1.13.11.5 Hemogentisate 1,2-dioxygenaseHOMOGEN+O2->MACAC tyrdegb 5.2.1. 2 Maleyl-acetoacetate isomerase MACAC-> FUACAC tyrdegc 3.7.1. 2 Fumarylacetoacetase FUACAC-> FUM + ACTAC trydegd YDR268w 6. 1. 1. 2 MSWI tryptophanyl-tRNA synthetase, mitochondrial ATPm + TRPm + TRNAm-> AMPm + PPIm + mswl TRPTRNAm YDR242W 3. 5. 1. 4 AMD2 putative amidase PAD->PAC+NH3 and2_2 YDR242W 3. 5. 1. 4 AMD2 putative amidase IAD->IAC+NH3 and2_3 -2. 6. 1. 29 Diamine transaminase SPRMD + ACCOA-> ASPERMD + COA spra 1. 5. 3. 11 Polyamine oxidase ASPERMD + 02-> APRUT + APROA + H202 sprb -1. 5. 3. 11 Polyamine oxidase APRUT + 02-> GABAL + APROA + H202 sprc -2. 6. 1. 29 Diamine transaminase SPRM + ACCOA-> ASPRM + COA sprd 1.5.3.11 Polyamine oxidase ASPRM+O2->APERMA+APROA+H2O2 spre Prollne blosynthesis YDR300C 2.7. 2.11 prol gamma-glutamyl kinase, glutamate kinase GLU+ATP->ADP+GLUP pro1 YOR323C 1.2.1.41 PRO2 gamma-glutamyl phosphate reductase GLUP+NADH->NAH+PI+GLUGSAL pro2_1 YOR323C 1. 2. 1. 41 pro2 gamma-glutamyl phosphate reductase GLUP+NADPH->NADP+PI + GLUGSAL pro2 2 spontaneous conversion (Strathem) GLUGSAL <-> P5C gpsl spontaneous conversion (Strathem) GLUGSALm <-> P5Cm gps2 YER023W 1. 5. 1. 2 pro3 Pyrroline-5-carboxylate reductase P5C + NADPH-> PRO + NADP pro3 1 YER023W 1. 5. 1. 2 pro3 Pyrroline-5-carboxylate reductase PHC+NADPH->HPRO+NADP pro3_3 YER023W 1. 5. 1. 2 pro3 Pyrroline-5-carboxylate reductase PHC+NADH->HPRO+NAD pro3_4 YLR142W 1.5.3.- PUT1 Proline oxidase PROm+NADm->P5Cm+NADHm pro3_5 Metabolism of Other Amino Acids beta-Alanine metabolism 1. 2. 1. 3 aldehyde dehydrogenase, mitochondrial I GABALm + NADm-> GABAm + NADHm aldl YER073W 1.2.1.3 ALD5 mitochondrial Aldehyde Dehydrogenase LACALm + NADm <-> LLACm + NADHm ald5_2 Cyanoamino acid metabolism YJL126W 3. 5. 5.1 NIT2 NITRILASE APROP -> ALA + NH3 nit2_1 YJL126W 3. 5. 5. 1 NIT2 NITRILASE ACYBUT -> GLU + NH3 nit2_2 Proteins, Peptides and Aminoacids Metabolism YLR195C 2. 3. 1. 97 nmtl Glycylpeptide N-tetradecanoyltransferase TCOA + GLP-> COA + TGLP nmtl YDL040C 2. 3. 1. 88 natl Peptide alpha-N-acetyltransferase ACCOA + PEPD -> COA + APEP nat1 YGR147C 2. 3. 1. 88 NAT2 Peptide alpha-N-acetyltransferae ACCOA + PEPD -> COA + APEP nat2 Glutathione Blosynthesis YJLIOIC 6.3. 2.2 GSHI gamma-glutamylcysteine synthetase CYS + GLU + ATP -> GC + PI + ADP gsh1 YOL049W 6.3. 2.3 GSH2 Glutathione Synthetase GLY + GC + ATP-> RGT + PI + ADP gsh2 YBR244W 1. 11. 1. 9 GPX2 Glutathione peroxidase 2 RGT + H2O2 <-> OGT gpx2 YIR037W 1. 11. 1. 9 HYRI Glutathione peroxidase 2 RGT + H2O2 <-> OGT hyr1 YKL026C 1. 1t. 1. 9 GPXI Glutathione peroxidase 2 RGT+H202 <-> OGT gpxl YPL091W 1. 6.4. 2 GLRI Glutathione oxidoreductase NADPH + OCT -> NADP + RGT gk1 YLR299W 2.3. 2.2 ECM38 gamma-glutamyltranspeptidase RGT + ALA-> CGLY + ALAGLY ecm38 Metabollsm of Complex Carbohydrates Starch and sucrose metabolism YGR032W 2.4. 1.34 GSC2 1,3-beta-Glucan synthase UDPG -> 13GLUCAN + UDP gsc2 YLR342W 2.4. 1. 34 FKS1 1,3-beta-Glucan synthase UDPG -> 13GLUCAN + UDP fks1 YGR306W 2.4. 1. 34 FKS3 Protein with similarity to Fkslp and Gsc2p UDPG->13GLUCAN + UDP fks3 YDR261C 3. 2. 1. 58 exg2 Exo-1, 3-b-glucanase 13GLUCAN -> GLC exg2 YGR282C 3. 2. 1. 58 BGL2 Cell wall endo-beta-1, 3-glucanase 13GLUCAN-> GLC bgl2 YLR300W 3. 2. 1. 58 exgl Exo-1, 3-beta-glucanase 13GLUCAN -> GLC exg1 YOR19OW 3. 2. 1. 58 sprl sporulation-specific exo-1,3-beta-glucanase 13GLUCAN -> GLC spr1 Glycoprotein Biosynthesis/Degradation YMR013C 2. 7. 1. 108 sec59 Dolichol kinase CTP + DOL -> CDP + DOLP sec59 YPRI83W 2. 4. 1. 83 DPM1 Dolichyl-phosphatebeta-D-mannosyltransferase GDPMAN+DOLP->GDP+DOLMANP dpml YAL023C 2. 4. 1. 109 PMT2 Dolichyl-phosphate-mannose--protein DOLMANP->DOLP+MANNAN pmt2 mannosyltransferase YDL093W 2. 4.1.109 PMT5 Dolichyl-phosphate-mannose-protein DOLMANP -> DOLP + MANNAN pmt5 mannosyltransferase YDL095W 2. 4. 1. 109 PMTI Dolichyl-phosphate-mannose--protein DOLMANP->DOLP+MANNAN pmtl mannosyltransferase YGR199W 2. 4. 1. 109 PMT6 Dolichyl-phosphate-mannose--protein DOLMANP->DOLP+MANNAN pmt6 mannosyltransferase YJR143C 2. 4. 1. 109 PMT4 Dolichyl-phosphate-mannose--protein DOLMANP-> DOLP + MANNAN pmt4 mannosyltransferase YOR321W 2. 4. 1. 109 PMT3 Dolichyl-phosphate-mannose--protein DOLMANP-> DOLP + MANNAN pmt3 mannosyltransferase YBR199W 2. 4. 1. 131 KTR4 Glycolipid2-alpha-mannosyltransferase MAN2PD + 2GDPMAN->2GDP+2MANPD ktr4 YBR205W 2. 4. 1. 131 KTR3 Glycolipid2-alpha-mannosyltransferase MAN2PD + 2GDPMAN->2GDP+2MANPD ktr3 YDR483W 2. 4. 1. 131 kre2 Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD krc2 YJL139C 2. 4. 1. 131 yurl Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN -> 2 GDP + 2MANPD yur1 YKR061W 2. 4. 1. 131 KTR2 Glycolipid 2-alpha-mannosyltransferase MAN2PD 2GDPMAN->2GDP+2MANPD ktr2 YOR099W 2. 4. 1. 131 KTRI Glycolipid 2-alpha-mannosyltransferase MAN2PD + 2 GDPMAN-> 2 GDP + 2MANPD ktrl YPL053C 2. 4. 1. 131 KTR6 Glycolipid2-alpha-mannosyltransferase MAN2PD+2GDPMAN->2GDP+2MANPD ktr6 Aminosugars metabolism YER062C 3. 1. 3. 21 HOR2 DL-glycerol-3-phosphatase GL3P->GL+PI hor2 YIL053W 3. 1. 3. 21 RHR2 DL-glycerol-3-phosphatase GL3P -> + PI rhr2 YLR307W 3. 5.1.41 CDA1 Chitin Deacetylase CHIT -> CHITO + AC eda1 YLR308W 3. 5. 1. 41 CDA2 Chitin Deacetylase CHIT-> CHITO + AC cda2 Metabolism of Complex Lipids Glycerol (Glycerolipid metabolism) YFL053W 2. 7. 1. 29 DAK2 dihydroxyactone kinase GLYN + ATP -> T3P2 + ADP dak2 YML070W 2. 7. 1. 29 DAKI putative dihydroxyacetone kinae GLYN + ATP -> T3P2 + ADP dak1 YDL022W 1. 1. 1. 8 GPDI glycerol-3-phosphatedehydrogenase (NAD) T3P2+NADH->GL3P+NAD gpdl YOL059W 1.1.1.8 GPD2 glycerol-3-phosphate dehydrogenase (NAD) T3P2 + NADH-> GL3P + NAD gpd2 YHL032C 2.7. 1. 30 GUTI glycerol kinase GL + ATP -> GL3P + ADP gut1 YIL155C 1. 1. 99.5 GUT2 glycerol-3-phosphatcdehydrogenase GL3P+FADm->T3P2+FADH2m gut2 DAGLY + 0. 017 CIOOACP + 0.062 C120ACP + daga 0. 100 C140ACP + 0.270 C160ACP + 0. 169 C l61 ACP + 0. 055 C180ACP + 0.235 C ACP + 0.093 C182ACP -> TAGLY + ACP Metabollsm of Cofactors, Vitamins, and Other Substances Thiamine (Vitamin B1) metabolism YOR143C 2.7. 6.2 TH180 Thiaminpyrophosphokinase ATP+THIAMINaAMP+TPP thi80_I YOR143C 2.7. 6.2 TH180 Thiamin pyrophosphokinase ATP + TPP -> AMP + TPPP thi80_2 -thiC protein AIR-> AHM thic YOL055C 2. 7. 1.49 TH120 Bipartite protein consisting of N-terminal AHM + ATP-> AHMP + ADP thi20 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- termina ! Pett8p-iike domain of indeterminant function YPL258C 2. 7. 1. 49 TH121 Bipartite protein consisting of N-terminal AHM + ATP -> AHMP + ADP thi21 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Petl8p-like domain of indeterminant function YPR121W 2.7. 1. 49 TH122 Bipartite protein consisting of N-terminal AHM + ATP-> AHMP + ADP thi22 hydroxymethylpyrimidine phosphate (HMP-P) kinase domain, needed for thiamine biosynthesis, fused to C- terminal Petl8p-like domain of indeterminant function YOL055C 2.7. 4.7 TH120 HMP-phosphatekinase AHMP+ATP->AHMPP+ADP thid Hypothetical T3PI + PYR-> DTP unkrxn I thiG protein DTP+TYR+CYS->THZ+HBA+C02 thig <BR> <BR> thiE protein DTP+TYR+CYS->THZ+HBA+C02 thie thiF protein DTP+TYR+CYS->THZ+HBA+C02 thif -thiH protein DTP + TYR + CYS -> THZ + HBA + CO2 thih YPL214C 2.7. 1. 50 TH16 Hydroxyethylthiazole kinase THZ + ATP-> THZP + ADP thim YPL214C 2.5. 1. 3 TH16 TMP pyrophosphorylase, hydroxyethylthiazole kinase THZP + AHMPP -> THMP + PPI thi6 - 2. 7. 4. 16 Thiamin phosphate kinase THMP + ATP <-> TPP + ADP thil 3. 1. 3. - (DL)-glycerol-3-phosphatase 2 THMP-> THIAMIN + PI unkrxn8 Riboflavin metabolism YBL033C 3. 5. 4.25 rib GTP cyclohedrolase II GTP -> D6RP5P + FOR + PPI rib1 YBR153W 3.5. 4.26 RIB7 HTP reductase, second step in the riboflavin D6RP5P-> A6RP5P + NH3 ribdl biosynthesis pathway YBR153W 1. 1. 1. 193 rib7 Pyrimidine reductase A6RP5P + NADPH-> A6RPSP2+NADP rib7 Pyrimidine phosphatase A6RP5P2-> A6RP + PI prm - 3. 4 Dihydroxy-2-butanone-4-phosphate synthase RLSP-> DB4P + FOR ribb YBR256C 2. 5. 1. 9 RIB5 Riboflavin biosynthesis pathway enzyme, 6, 7-dimethyl- DB4P + A6RP -> D8RL + PI rib5 8-ribityllumazine synthase, apha chain YOL I 43C 2. 5. 1. 9 RIB4 Riboflavin biosynthesis pathway enzyme, 6, 7-dimethyl- 8-ribityllumazine synthase, beta chain YAR07 W 3. 1. 3. 2 phol I Acid phosphatase FMN-> RIBFLAV + pI phol I YDR236C 2.7. 1.26 FMN1 Riboflavin kinase RIBFLAV + ATP -> FMN + ADP fmn1_1 YDR236C 2.7. 1.26 FMN1 Riboflavin kinase RIBFLAVm + ATPm -> FMNm + ADPm fmn1_2 YDL045C 2.7. 7.2 FAD FADsynthetase FMN+ATP->FAD+PPI fadl 2.7. 7.2 FAD synthetase FMNm + ATPm-> FADm + PPlm fadib Vitamin B6 (Pyridoxine) Biosynthesis metabolism - 2. 7. 1. 35 Pyridoxine kinase PYRDX + ATP-> P5P + ADP pdxka - 2. 7. 1. 35 Pyridoxine kinase PDLA + ATP -> PDLA5P + ADP pdxkb - 2.7.1. 35 Pyridoxine kinase PL + ATP-> PL5P + ADP pdxkc YBR035C 1. 4.3. 5 PDX3 Pyridoxine S'-phosphate oxidase PDLA5P + 02-> PLSP + H202 + NH3 pdx3_1 YBR035C 1. 4.3. 5 PDX3 Pyridoxine 5'-phosphate oxidase P5P + O2 <-> PL5P + H2O2 pdx3_2 YBR035C 1. 4.3. 5 PDX3 Pyridoxine 5'-phosphate oxidase PYRDX + O2 <-> PL + H2O2 pdx3_3 YBR035C 1. 4.3. 5 PDX3 Pyridoxine 5'-phosphate oxidase PL+02+NH3<->PDLA+H202 pdx34 YBR035C 1. 4.3. 5 PDX3 Pyridoxine 5'-phosphate oxidase PDLA5P + 02-> PL5P + H202 + NH3 pdx3 5 YOR184W 2.6. 1. 52 serl Hypothetical trnasaminase/phosphoserine transaminase OHB + GLU <-> PHT + AKG scr1_2 YCR053W 4.2. 99.2 thr4 Threonine synthase PHT -> 4HT + PI thr4_2 3. 1. 3.- Hypothetical Enzyme PDLA5P -> PDLA + PI hor2b Pantothenate and CoA blosynthesis 3 MALCOA-> CHCOA + 2 COA + 2 C02 biol - 2. 3. 1. 47 8-Amino-7-oxononanoate synthase ALA + CHCOA <-> C02 + COA + AONA biof YNR058W 2. 6. 1. 62 B103 7, 8-diamino-pelargonic acid aminotransferase (DAPA) SAM + AONA <-> SAMOB + DANNA bio3 aminotransferase YNR057C 6.3. 3.3 B104 dethiobiotin synthetase CO2 + DANNA + ATP <-> DTB + PI + ADP bio4 YGR286C 2.8. 1. 6 B102 Biotin synthase DTB + CYS <-> BT bio2 Folate biosynthesis YGR267C 3. 5. 4. 16 fol2 GTPcyclohydrolasel GTP->FOR+AHTD fol2 - 1.6.1.- Dihydroneopterin triphosphate pyrophosphorylase AHTD -> PPI + DHPP ntpa YDR481C 3.1. 3. 1 pho8 Glycerophosphatase, Alkaline phosphatase; Nucleoside AHTD -> DHP + 3 PI pho8 triphosphatase YDLIOOC 3. 6.1.- YDL100 Dihydroncopterin monophosphate dephosphorylase DHPP -> DHP + PI dhdnpa C YNL256W 4. 1. 2. 25 foll Dihydroneopterin aldolase DHP-> AHHMP + GLAL foll I YNL256W 2.7. 6.3 fol1 6-Hydroxymethyl-7,8 dihydropterin pyrophosphokinase AHHMP + ATP -> AMP + AHHMD fol1_2 YNR033W 4. 1. 3.- ABZ1 Aminodeoxychorismate synthase CHOR + GLN -> ADCHOR + GLU abz1 4--,-Aminodeoxychorismate Iyase ADCHOR-> PYR + PABA pabc YNL256W 2.5. 1.15 foll Dihydropteroate synthase PABA + AHHMD -> PPI + DHPT fol1_3 YNL256W 2. 5.1.15 foll Dihydropteroate synthase PABA + AHHMP -> DHPT fol1_4 - 6. 3.2. 12 Dihydrofolate synthase DHPT + ATP + GLU -> ADP + P1 + DHf folc YOR236W 1. 5. 1. 3 dfrl Dihydrofolate reductase DHFm + NADPHm -> NADPm + THFm dfr1_1 <BR> <BR> YOR236W 1. 5. 1. 3 dfrl Dihydrofolatereductase DHF+NADPH->NADP+THF dfrl 2 6. 3.3. 2 5-Formyltetrahydrofolate cyclo-ligase ATPm + FTHFm-> ADPm + Plm + MTHFm ftfa - 6. 3.3. 2 5-Formyltetrahydrofolate cyclo-ligase ATP + FTHF -> ADP + PI + MTHF ftfb YKL132C 6.3. 2. 17 RMAI Protein with similarity to folylpolyglutamate synthase ; THF + ATP + GLU <-> ADP + Pl + THFG mmal converts tctrahydrofolyl- [Glu (n) ] + glutamate to tetrahydrofolyl-[Glu(n+1)] YMR113W 6.3. 2. 17 FOL3 Dihydrofolate synthetase THF + ATP + GLU <-> ADP + PI + THFG fol3 YOR241 W 6.3. 2.17 MET7 Folylpolyglutamate synthetase, involved in methionine THF + ATP + GLU <-> ADP + PI + THFG met7 biosynthesis and maintenance of mitochondrial genome One carbon pool by folate IMAP : 006701 YPL023C 1. 5. 1. 20 MET12 Methylene tetrahydrofolate reductase METTHFm + NADPHm-> NADPm + MTHFm metl2 YGL125W 1. 5. 1. 20 metl3 Methylenetetrahydrofolatereductase METTHFm+NADPHm->NADPm+MTHFm metl3 YBR084W 1. 5. 1. 5 misl themitochondrial trifunctional enzymeCl-METTHFm+NADPm<->METHFm+NADPHm misl I tetrahydroflate synthase YGR204W 1. 5. 1. 5 ade3 thecytoplasmictrifunctionalenzymeCl-METTHF+NADP<->METH F+NADPH ade3_I tetrahydrofolate synthase YBR084W 6.3. 4.3 misl the mitochondrial trifunctional enzyme C1- THFm + FORm + ATPm -> Plm + FTHFm mis1_2 tetrahydroflate synthase YGR204W 6.3. 4.3 ade3 the cytoplasmic trifunctional enzyme C1- THF + FOR + ATP -> ADP + PI + FTHF adc3_2 tetrahydrofolate synthase YBR084W 3.5. 4.9 misl the mitochondrial trifunctional enzyme Cl-METHFm <-> FTHFm misl 3 tetrahydroflate synthase YGR204W 3. 5. 4.9 ade3 thecytoplasmictnfunctionalenzymeCI-METHF<->FTHF ade3 3 tetrahydrofolate synthase YKR080W 1. 5. 1. 15 MTDI NAD-dependent5, 10-methylenetetrahydrafolate METTHF+NAD->METHF+NADH mtdl dehydrogenase YBL013W 2. 1. 2.9 fmtl Methionyl-tRNA Transformylase FTHFm + MTRNAm-> THFm + FMRNAm fmtl Coenzyme A Biosynthesis YBR176W 2. 1. 2. 11 ECM31 Ketopentoate hydroxymethyl transferase OIVAL + MEITHF -> AKP + THF ecm31 <BR> <BR> YHR063C 1. 1. 1. 169 PANS Putative ketopantoate reductase (2-dehydropantoate 2-AKP + NADPH-> NADP + PANT pane reductase) involved in coenzyme A synthesis, has similarity to Cbs2p, Ketopantoate reductase YLR355C 1. 1. 1. 86 ilv5 Ketol-acidreductoisomerase AKPm+NADPHm->NADPm+PANTm ilv5 3 YIL145C 6.3. 2. 1 YIL145C Pantoate-b-alanine ligase PANT + bALA + ATP -> AMP + PPI + PNTO panca YDR531W 2. 7. 1. 33 YDR531 Putative pantothenate kinase involved in coenzyme A PNTO + ATP -> ADP + 4PPNTO coaa W biosynthesis, Pantothenate kinase - 6. 3.2. 5 Phosphopantothenate-cysteineligase 4PPNTO ++CTP+CYS->CMP + PPI +4PPNCYS pclig - 4.1.1. 36 Phosphopantothenate-cysteine decarboxylase 4PPNCYS -> CO2 + 4PPNTE pedel - 2. 7.7. 3 Phospho-pantethiene adenylyltransferase 4PPNTE + ATP-> PPI + DPCOA patrana 2. 7.7. 3 Phospho-pantethiene adenylyltransferase 4PPNTEm + ATPm-> PPIm + DPCOAm patranb -2. 7. 1. 24 DephosphoCoA kinase DPCOA + ATP-> ADP + COA dphcoaka - 2.7.1. 24 DephosphoCoA kinase DPCOAm + ATPm-> ADPm + COAm dphcoakb -4. 1. 1. 11 ASPARTATEALPHA-DECARBOXYLASE ASP->C02+bALA pancb YPL148C 2.7. 8.7 PPT2 Acyl carrier-protein synthase, phosphopantetheine COA-> PAP + ACP acps protein transferase for Acp1p NAD Biosynthesis YGL037C 3. 5. 1. 19 PNCI Nicotinamidase NAM<->NAC+NH3 nadh YOR209C 2.4. 2. 11 NPT1 NAPRTase NAC + PRPP-> NAMN + PPI nptl 1. 4. 3.- Aspartate oxidase ASP + FADm -> FADH2m + ISUCC nadb 1. 4. 3. 16 Quinolate synthase ISUCC + T3P2-> Pl + QA nada YFR047C 2.4. 2. 19 QPTI Quinolate phosphoribosyl transferase QA + PRPP -> NAMN + CO2 + PPI nadc YLR328W 2.7. 7. 18 YLR328 Nicotinamide mononucleotide (NMN) NAMN + ATP -> PPI + NAAD nadd1 W adenylyltransferase YHR074W 6.3. 5. 1 QNS I Deamido-NAD ammonia ligase NAAD + ATP + NH3-> NAD + AMP + PPI nade YJR049c 2.7. 1.23 utr1 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + ADP nodf 1 2.7. 4.1)/NAD+ KINASE (EC 2. 7. 1. 23) YEL041w 2. 7. 1. 23 YEL041 NAD kinase, POLYPHOSPHATE KINASE (EC NAD + ATP -> NADP + ADP nadf 2 w 2.7. 4. 1)/NAD+ KINASE (EC 2.7. 1. 23) YPL188w 2.7. 1.23 POSS NAD kinase, POLYPHOSPHATE KiNASE (EC NAD+ATP->NADP+ADP nadf5 2.7. 4. 1)/NAD+ KiNASE (EC 2.7. 1.23) 3. 1. 2,-NADP phosphatase NADP-> NAD + Pl nadphps 3.2. 2. 5 NAD-> NAM + ADPRIB nadi 2.4. 2. 1 strong similarity to purine-nucleoside phosphorylases ADN + Pi <-> AD + RIP nudg1 2.4. 2. 1 strong similarity to purine-nucleoside phosphorylases GSN + Pl <-> GN + RIP nadg2 Nlcotinic Acid synthesis from TRP YFR047C 2.4. 2. 19 QPTI Quinolate phosphoribosyl transferase QAm + PRPPm-> NAMNm + C02m + PPlm mnadc YLR328W 2.7. 7. 18 YLR328 NAMNadenylyltransferase NAMNm+ATPm->PPIm+NAADm mnaddl W YLR328W 2.7. 7. 18 YLR328 NAMN adenylyl transferase NMNm + ATPm -> NADm + PPIm mnadc2 W YHR074W 6.3. 5.1 QNS1 Deamido-NAD ammonia ligase NAADm + ATPm + NH3m -> NADm + AmPm + mnade PPIm YJR049c 2. 7. 1. 23 utr1 NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm -> NADPm + ADPm mnadf 1 2.7. 4. 1)/NAD+ KINASE (EC 2.7. 1. 23) YPL188w 2. 7. 1. 23 POS5 NAD kinase, POLYPHOSPHATE KINASE (EC NADm+ATPm->NADPm+ADPm mnadf 2 2.7. 4. 1)/NAD+KINASE (EC 2. 7. 1. 23) YEL041w 2. 7. 1. 23 YEL041 NAD kinase, POLYPHOSPHATE KINASE (EC NADm + ATPm-> NADPm + ADPm mnadf 5 w 2.7. 4. 1)/NAD+ KiNASE (EC 2. 7. 1. 23) . 3.1.2.- NADP phosphstase NADPm -> NADm + PIm mnadphps YLR209C 2.4. 2.1 PNP1 strong similarity to purine-nucleoside phosphoryalses ADNm + PIm <-> ADm + RIPm mnadg1 YLR209C 2.4. 2. 1 PNPI strong similarity to purine-nucleoside phosphorylases GSNm + Plm <-> GNm + RlPm mnadg2 YGL037C 3. 5. 1. 19 PNCI Nicotinamidase NAMm<->NACm+NH3m mnadh YOR209C 2.4. 2. 11 NPT1 NAPRTase NACm + PRPPm -> NAMNm + PPIm mnpt1 3.2. 2. 5 NADm-> NAMm + ADPRIBm mnadi Uptake Pathways Porphyrln and Chlorophyll Metabolism YDR232W 2. 3. 1. 37 heml 5-Aminolevulinate synthase SUCCOAm + GLYm-> ALAVm + COAm + C02m heml YGL040C 4. 2. 1. 24 HEM2 Aminolevulinatedehydrease 2ALAV->PBG hem2 YDL205C 4. 3. 1. 8 HEM3 Hydroxymethylbilane synthase 4 PBG -> HMB + 4 NH3 hcm3 YOR278W 4. 2. 1. 75 HEM4 Uroporphyrinogen-Ill synthase HMB-> UPRG hem4 YDR047W 4. 1. 1. 37 HEM12 Uroporphyrinogendecarboxylase UPRG->4C02+CPP heml2 YDR044W 1. 3.3. 3 HEM13 Coproporphyrinogen oxidase, aerobic 02 + CPP-> 2 C02 + PPHG heml3 YER014W 1. 3.3. 4 HEM14 Protopomhyrinogen oxidase 02 + PPHGm-> PPIXm heml4 YOR176W 4. 99.1.1 HEM15 Ferrochelatase PPIXm -> PTHm bem15 YGL245W 6. 1. 1. 17 YGL245 glutamyl-tRNA synthetase, cytoplasmic GLU + ATP-> GTRNA + AMP + PPI unrxnl0 W YOL033W 6.1.1.17 MSE1 GLUm + ATPm -> GTRNAm + AMPm + PPIm mse1 YKR069W 2.1.1.107 met1 uroporphyrin-III C-methyltransferase SAM + UPRG -> SAH + PC2 met1 Qulnone Blosynthesls YKL211C 4.1.3. 27 trp3 anthranilatesynthaseComponentllandindole-3-CHOR->4HBZ+PYR trp3 3 phosphate (multifunctional enzyme) YER090W 4. 1. 3.27 trp2 anthranilate synthase Component 1 CHOR -> 4HBZ + PYR trp2_2 YPR176C 2. 5.1.- BET2 geranylgeranyltransferase type II beta subunit 4HBZ + NPP -> N4HBZ + PPI bet2 YJL03 I C 2. 5.1.- BET4 genanylgeranyltransferase type II alpha subunit YGL155W 2. 5.1.- cdc43 geranylgeranyltransferase type 1 beta subunit YBR003W 2. 5. 1.-COQI Hexaprenylpyrophosphatesynthctase, catalyzesthe 4HBZ+NPP->N4HBZ+PPI coql first step in coenzyme Q (ubiquinone) biosynthesis pathway YNR041C 2. 5. 1.-COQ2 para-hydroxybenzoate--polyprenyltransferase 4HBZ+NPP->N4HBZ+PPI coq2 YPL172C 2. 5.1.- COX10 protoheme IX famesyltransferase, mitochondrial 4HBZ + NPP -> N4HBZ + PPI cox10 precursor YDL090C 2. 5.1.- rml protein famesyltransferase beta subunit 4HBZ + NPP -> N4HBZ + PPI ram1 YKL019W 2. 5. 1.-RAM2 protein famesyltransferase alpha subunit YBR002C 2. 5.1.- RER2 putative dehydrodolichyl diphospate synthetase 4HBZ + NPP -> N4HBZ + PPI ter2 YMRIOIC 2. 5. 1 : SRTI putativedehydrodolichyldiphospatesynthetase 4HBZ+NPP->N4HBZ++P1 srtl YDR538W 4. 1. 1.-PADI Octaprenyl-hydroxybenzoatedecarboxylase N4HBZ->CO2+2NPPP padl 2 - 1.13.14.- 2-Octaprenylphenol hydroxylase 2NPPP + O2 -> 2N6H ubib YPL266W 2.1.1.- DIM1 2N6H + SAM -> 2NPMP + SAH dim1 - 1.14.13.- 2-Octaprenyl-6-methoxyphenol hydroxylase 2NPMPm + O2m -> 2 NPMBm ubih YML110C 2.1.1.- COQ5 2-Octaprenyl-6-methoxy-1,4-benzoquinone methylase 2NPMBm + SAMm -> 2NPMMBm + SAHm coq5 YGR255C 1.14.13.- COQ6 COQ6 monooxygenase 2NPMMBm + O2m -> 2NMHMBm coq6b YOL096C 2. 1. 1. 64 COQ3 3-Dimethylubiquinone 3-methyltransferase 2NMHMBm + SAMm-> QH2m + SAHm ubig Memberane Transport Mltochondiral Membrane Transport The followings diffuse through the inner mitochondiral membrane in a non-carrier-mediated manner : 02 <-> 02m mo2 C02 <-> C02m mco2 ETH <-> ETHm meth NH3 <-> NH3m mnh3 MTHN <-> MTHNm mmthn THFm<->THF mthf METTHFm <-> METTHF mmthf SERm <-> SER mser <BR> <BR> GLYm <-> GLY mgly<BR> <BR> <BR> <BR> CBHCAPm <-> CBHCAP mcbh OICAPm <-> OICAP moicap PROm <-> PRO mpro CMPm <-> CMP mcmp ACm <-> AC mac <BR> <BR> <BR> ACAR-> ACARm macar<BR> <BR> <BR> <BR> CARm-> CAR mcar<BR> <BR> <BR> <BR> ACLAC <-> ACLACm maclac ACTAC <-> ACTACm mactc SLF-> SLFm + Hm mslf THRm <-> THR mthr AKAm-> AKA maka YMR056c AAC I ADP/ATP carrier protein (MCF) ADP + ATPm + PI -> Hm + ADPm + ATP + PIm nac1 YBL030C pet9 ADP/ATP carrier protein (MCF) ADP + ATPm + Pl-> Hm + ADPm + ATP + Plm pet9 YBR085w AAC3 ADP/ATP carrier protein (MCF) ADP + ATPm + P1 -> Hm - ADPm + ATP + PIm aac3 YJR077C MIRI phosphate carrier PI <-> Hm + PIm mir1a YER053C YER053 similarity to C. elegans mitochondrial phosphate carrier PI + OHm <-> PIm mirld C YLR348C DIC I dicarboxylate carricr MAL + SUCCm <-> MALm + SUCC dicl YLR348C DIC1 dicarboxylate carrier MAL + PIm <-> MALm + P1 dic1_2 YLR348C DICI dicarboxylatecarricr SUCC+Plm->SUCCm+PI dicl 3 MALT + PIm <-> MALTm + PI mmlt YKL120W OAC1 Mitochondrial oxaloacetat carrier OA <-> OAm + Hm moab <BR> <BR> YBR291C CTPI citratetransportprotein CIT+MALm<->CITm+MAL ctpl-1 YBR291C CTP1 citrate transport protein CIT + PEPm <-> CITm + PEP ctp1_2 YBR29) C CTP) citrate transport protein CIT + ICITm <-> CITm + ICIT ctp1_3 IPPMAL <-> IPPMALm mpmalR LAC <-> LACm + Hm mlac pyruvate carrier PYR <-> PYRm + Hm pyrca glutamate carrier GLU <-> GLUm + Hm gca GLU + OHm-> GLUm gcb YOR130C ORTI omithine carrier ORN + Hm <-> ORNm ortl YORIOOC CRCI carnitine carrier CARm + ACAR-> CAR + ACARm crcl OIVAL <-> OIVALm moival OMVAL <-> OMVALm momval YIL134W FLX1 Protein involved in transport of FAD from cytosol into FAD + FMNm -> FADm + FMN mfad the mitochondrial matrix RIBFLAV <-> RIBFLAVm mribo DTB <-> DTBm mdtb H3MCOA <-> H3MCOAm mmcoa MVL <-> MVLm mmvl PA <-> PAm mpa 4PPNTE <-> 4PPNTEm mppnt AD <-> ADm mad PRPP <-> PRPPm mprpp DHF <-> DHFm mdhf QA <-> QAm mqa OPP <-> OPPm mopp SAM <-> SAMm msam SAH <-> SAHm msah YJR095W SFCI Mitochondrial membrane succinate-fumarate SUCC + FUMm-> SUCCm + FUM sfc I transporter, member of the mitochondrial carrier family (MCF) of membrane transporters YPL134C ODCI 2-oxodicarboylate transporter AKGm + OXA <-> AKG + OXAm odcl YOR222W ODC2 2-oxodicarboylate transporter AKGm + OXA <-> AKG + OXAm odc2 Malate Aspartate Shuttle Included elsewhere Glycerol phosphate shuttle T3P2m-> T3P2 mt3p GL3P-> GL3Pm mgl3p Plasma Membrane Transport Carbohydrates YHR092c HXT4 moderate-to low-affinity glucose transporter GLCxt-> GLC hxt4 YLR081w GAL2 galactose (and glucose) permease GLCxt -> GLC gal2_3 YOL156w HXT11 low affinity glucose transport protein GLCxt-> GLC hxtl I YDR536W stl1 Protein member of the hexose transporter family GLCxt -> GLC stl1_1 YHR094c hxtl High-affinity hexose (glucose) transporter GLCxt-> GLC hxtl_l YOL156w HXT11 Glucose permease GLCxt -> GLC hxt11_1 YEL069c HXT13 high-affinity hexose transporter GLCxt -> GLC hxt13_1 YDL245c HXT15 Hexose transporter GLCxt -> GLC hxt15_1 YJR158w HXT16 hexosepermease GLCxt->GLC hxtl6 1 YFL011w HXT10 high-affinity hexose transporter GLCxt > GLC hxt10_1 YNR072w HXT17 Putative hexose transporter GLCxt-> GLC hxtl7 1 YMR011w HXT2 high affinity hexose transporter-2 GLCxt -> GLC hxt2_1 YHR092c hxt4 High-affinity glucose transporter GLCxt->GLC hxt4 YDR345c hxt3 Low-affinity glucose transporter GLCxt->GLC hxt3 YHR096c HXT5 hexose transporter GLCxt->GLC hxt5 YDR343c HXT6 Hexose transporter GLCxt-> GLC hxt6_1 YDR342c HXT7 Hexose transporter GLCxt->GLC hxt7 YJL214w HXT8 hexose permease GLCx1 -> GLC hxt8_4 YJL219w HXT9 hexosepermease GLCxt->GLC hxt9 YLR081w gal2 galactosepermease GLACxt+HEXT->GLAC gal2 I YFL011w HXT10 high-affinity hexose transporter GLACxt + HeXT -> GLAC hxt10_4 YOL156w HXT11 Glucose permease GLACxt + HEXT -> GLAC hxt11_4 YNL318c HXT14 Member of the hexose transporter family GLACxt + HEXT -> GLAC hxt14 YJL219w HXT9 hexosepermease GLACxt+HEXT->GLAC hxt94 YDR536W stl I Protein member of the hexose transporter family GLACxt + HEXT -> GLAC stl1_4 YFL055w AGP3 Amino acid permease for serine, aspartate, and GLUxt+HEXT<->GLU agp33 glutamate YDR536W stl1 Protein member of the hexose transporter family GLUxt + HEXT <-> GLU stil 2 YKR039W gapl General amino acid permease GLUxt+HEXT <-> GLU gap8 YCL025C AGPI Amino acid permease for most neutral amino acids GLUxt + HEXT <-> GLU gap24 YPL265W DiP5 Dicarboxylic amino acid permease GLUxt + HEXT <-> GLU dipl0 YDR536W stl I Protein member of the hexose transporter family GLUxt + HEXT <-> GLU stl1_3 YHR094c hxtl High-affinity hexose (glucose) transporter FRUxt + HEXT -> FUIJ Hext1_2 YFL011w HXT10 high-affinity hexose transporter FRUx1 + HEXT -> FRU hxt10_2 YOL156w HXT11 Glucose permease FRUx1 + HEXT -> FRU hext11_2 YEL069c HXT13 high-affinityhexosetransporter FRUxt+HEXT->FRU hxtl3_2 YDL245c HXTIS Hexosetransporter FRUxt+HEXT->FRU hxtl5_2 <BR> <BR> YJR1S8w HXT16 hexosepermease FRUxt+HEXT->FRU hxtl6_2 YNR072w HXT17 Putative hexose transporter FRUxt + HEXT -> FRU hext17_2 YMR011w HXT2 high affinity hexose transporter-2 FRUxt+HEXT->FRU hxt22 YDR345c hxt3 Low-affinityglucosetransporter FRUxt+HEXT->FRU hxt32 YHR092c hxt4 High-affinity glucose transporter FRUxt + HEXT-> FRU hxt4 2 YHR096c HXT5 hexose transporter FRUxt + HEXT -> FRU hext5_2 YDR343c HXT6 Hexose transporter FRUxt+HEXT->FRU hxt62 YDR342c HXT7 Hexose transporter FRUxt+HEXT->FRU hxt72 YJL214w HXT8 hexosepermease FRUxt+HEXT->FRU hxt85 YJL219w HXT9 hexose permease FRUxt+HEXT->FRU hxt92 YHR094c hxtl High-affinity hexose (glucose) transporter MANxt + HEXT MAN hxtl_5 YFL011w HXT10 high-affinity hexose transporter MANxt + HEXT -> MAN hxt10_3 YOL156w HXT11 Glucose permease MANxt + HEXT -> MAN hxt11_3 YEL069c HXT13 high-affinityhexosetransporter MANxt+HEXT->MAN hxtl3_3 YDL245c HXT15 Hexosetransporter MANxt+HEXT->MAN hxtl5_3 YJR158w HXT16 hexose permease MANxt + HEXT -> MAN hxt16_3 YNR072w HXT17 Putative hexose transporter MANxt + HEXT -> MAN hext17_3 YMR011 w HXT2 high affinity hexose transporter-2 MANxt + HEXT -> MAN hxt23 YDR345c hxt3 Low-affinity glucose transporter MANxt + HEXT -> MAN hxt3_3 YHR092c hxt4 High-affinity glucose transporter MANxt + HEXT -> MAN hxt4_3 YHR096c HXTS hexose transporter MANxt + HEXT -> MAN hxt5_3 YDR343c HXT6 Hexosetransporter MANxt+HEXT-> MAN hxt63 YDR342c HXT7 Hexosetransporter MANxt+HEXT-> MAN hxt73 YJL214w HXT8 hexose permease MANxt+HEXT->MAN hxt86 YJL219w HXT9 hexose permease MANxt+HEXT-> MAN hxt93 YDR497c ITRI myo-inositol transporter Mlxt+HEXT->MI itrl YOL103w ITR2 myo-inositoltransporter Mlxt+HEXT->MI itr2 Maltase permease MLTxt + HEXT-> MLT mltup YIL162W 3.2. 1.26 SUC2 invertase (sucrose hydrolyzing enzyme) SUCxt -> GLCxt + FRUxt suc2 sucrose SUCxt + HEXT-> SUC sucup YBR298c MAL31 Dicarboxylates MALxt + HEXT <-> MAL mal31 a-Ketoglutarate/malate translocator MALxt + AKG <-> MAL + AKGxt akmup a-methylglucoside AMGxt <-> AMG amgup Sorbose SORxt <-> SOR sorup Arabinose (low affinity) ARABxt <-> ARAB arbup1 Fucose FUCxt + HEXT <-> FUC fucup GLTLxt + HEXT-> GLTL gltlupb Glucitol GLTxt + HEXT-> GLT gltup Glucosamine GLAMxt + HEXT <-> GLAM gaup YLL043W FPSI Glycerol GLxt<->GL glup <BR> <BR> YKL217W JENI Lactatetransport LACxt+HEXT<->LAC lacupl Mannitol MNTxt + HEXT-> MNT mntup Melibiose MELIxt+HEXT-> MELI melup_I N-Acetylglucosamine NAGxt + HEXT-> NAG nagup Rhamnose RMNxt + HEXT-> RMN rmnup Ribose RIBxt + HEXT -> RIB ribup Trehalose TRExt + HEXT-> TRE trcup_1 TRExt-> AATRE6P treup2 XYLxt <-> XYL xylup Amino Acids YKR039W gapl General amino acid permease ALAxt + HEXT <-> ALA gap1_1 YPL265W DiP5 Dicarboxylic amino acid permease ALAxt + HEXT <-> ALA dip5 YCL025C AGP I Amino acid permease for most neutral amino acids ALAxt + HEXT <-> ALA gap25 YOL020W TAT2 Tryptophan permease ALAxt + HEXT <-> ALA tat5 YOR348C PUT4 Proline permease ALAxt + HEXT <-> ALA put4 YKR039W gap1 General amino acid permease ARGxt + HEXT <-> ARG gap2 YEL063C can) Permease for basic amino acids ARGxt + HEXT <-> ARG canl I YNL270C ALP) Protein with strong similarity to permeases ARGxt +HEXT<->ARG alp I YKR039W gap General amino acid permease ASNxt + HEXT <-> ASN gap3 YCL025C AGPI Amino acid permease for most neutral amino acids ASNxt + HEXT <-> ASN gap21 YDR508C GNPI Glutamin permease (high affinity) ASNxt + HEXT <-> ASN gnp2 YPL265W DIP5 Dicarboxylic amino acid permease ASNxt + HEXT <-> ASN dip6 YFL055W AGP3 Amino acid permease for serine, aspartate, and ASPxt + HEXT <-> ASP agp3_2 glutamate YKR039W gap I General amino acid permease ASPxt + HEXT <-> ASP gap4 YPL265W DIP5 Dicarboxylic amino acid permease ASPxt + HEXT <-> ASP dip7 YKR039W gapl General amino acid permease CYSxt + HEXT <-> CYS gap5 YDR508C GNPI Glutamin permease (high affinity) CYSxt + HEXT <-> CYS gnp3 YBR068C BAP2 Branched chain amino acid permease CYSxt + HEXT <-> CYS bap2_1 YDR046C BAP3 Branched chain amino acid permease CYSxt + HEXT <-> CYS bap3_1 YBR069C VAPI Amino acid permease CYSxt + HEXT <-> CYS vap7 YOL020W TAT2 Tryptophan permease CYSxt + HEXT <-> CYS tat7 YKR039W gap General amino acid permease GLYxt + HEXT <-> GLY gap6 YOL020W TAT2 Tryptophan permease GLYxt+HEXT<->GLY tat6 YPL265W DIP5 Dicarboxylic amino acid permease GLYxt + HEXT <-> GLY dip8 YOR348C PUT4 Proline permease GLYxt + HEXT <-> GLY put5 YKR039W gapl General amino acid permease GLNxt + HEXT <-> GLN gap7 YCL025C AGP1 Amino acid permease for most neural amino acids GLNxt + HEXT <-> GLN gap22 YDR508C GNPI Glutamine permease (high affinity) GLNxt + HEXT <-> GLN gnpl YPL265W DIP5 Dicarboxylic amino acid permease GLNxt + HEXT <-> GLN dip9 YGR191W HIP1 Histidine permease HISxt + HEXT <-> HIS hip1 YKR039W gap General amino acid permease HlSxt + HEXT< HIS gap9 YCL025C AGPI Amino acid permease for most neutral amino acids HlSxt + HEXT <-> HIS gap23 YBR069C VAPI Amino acid permease HlSxt + HEXT <-> HIS vap6 YBR069C TAT Amino acid permease that transports valine, leucine, ILExt + HEXT <-> ILE tat1_2 isleucine, tyrosine, tryptophan, and threonine YKR039W gap1 General amino acid permease ILExt + HEXT <-> ILE gapl0 YCL025C AGPI Amino acid permease for most neutral amino acids ILExt + HEXT <-> ILE gap32 YBR068C BAP2 Branched chain amino acid permease ILExt + HEXT <-> ILE bap2 2 YDR046C BAP3 Branched chain amino acid permease) ILExt + HEXT <-> ILE bap3 2 YBR069C VAPI Amino acid permease ILExt + HEXT <-> ILE vap3 YBR069C TATI Amino acid permease that transports valine, leucine, LEUxt + HEXT <-> LEU tat 3 isleucine, tyrosine, tryptophan. and threonine YKR039W gap General amino acid permease LEUxt + HEXt <-> LEU gap11 YCL025C AGPI Amino acid permease for most neutral amino acids LEUxt + HEXT <-> LEU gap33 YBR068C BAP2 Branched chain amino acid permease LEUxt + HEXT <-> LEU bap2 3 YDR046C BAP3 Branched chain amino acid permease LEUxt + HEXT <-> LEU bap3_3 YBR069C VAP1 Amino acid permease LEUxt + HEXT <-> LEU vap4 YDR508C GNP I Glutamine permease (high affinity) LEUxt + HEXt <-> LEU gnp7 YKR039W gapl General amino acid permease METxt + HEXT <-> MET gapl3 YCL025C AGP1 Amino acid permease for most neutral amino acids METxt + HEXT <-> MET gap26 YDR508C GNPI Glutamin permease (high affinity) METxt + HEXT <-> MET gnp4 YBR068C BAP2 Branched chain amino acid permease METxt + HEXT <-> MET bap2_4 YDR046C BAP3 Branched chain amino acid permease METxt + HEXT <-> MET bap34 YGR055W MUPI High-affinity methionine permease METxt + HEXT <-> MET mup) YHL036W MUP3 Low-affinity methionine permease METxt + HEXT <-> MET mup3 YKR039W gap1 General amino acid permease PHExt + HEXT <-> PHEN gap14 YCL025C AGPI Amino acid permease for most neutral amino acids PHExt + HEXT <-> PHEN gap29 YOL020W TAT2 Tryptophan permease PHExt + HEXT <-> PHEN tat4 YBR068C BAP2 Branched chain amino acid permease PHExt + HEXT <-> PHEN bap2_5 YDR046C BAP3 Branched chain amino acid permease PHExt + HEXT <-> PHEN bap3_5 YKR039W gap Generalaminoacidpermease PROxt+HEXT<->PRO gapl5 YOR348C PUT4 Proline permease PROxt + HEXT <-> PRO put6 YBR069C TATI Amino acid permease that transports valine, leucine, TRPxt + HEXT <-> TRP tatl_6 isleucine, tyrosine, tryptophan, and threonine YKR039W gap I General amino acid permease TRPxt + HEXT <-> TRP gap 18 YBR069C VAPI Amino acid permease TRPxt+HEXT<->TRP vap2 YOL020W TAT2 Tryptophan permease TRPxt + HEXT <-> TRP tat3 YBR068C BAP2 Branched chain amino acid permease TRPxt+HEXT<->TRP bap26 YDR046C BAP3 Branched chain amino acid permease TRPxt + HEXT <-> TRP bap3_3 YBR069C TAT1 Amino acid permease that transports valine leucine, TYRxt + HEXT <-> TYR tatl_7 isleucine, tyrosine, tryptophan, and threonine YKR039W gap I General amino acid permease TYRxt + HEXT <-> TYR gapl9 YCL025C AGP) Amino acid permease for most neutral amino acids TYRxt + HEXT <-> TYR gap28 YBR068C BAP2 Branched chain amino acid permease TYRxt + HEXT <-> TYR bap2_7 YBR069C VAPI Amino acid permease TYRxt + HEXT <-> TYR vap I YOL020W TAT2 Tryptophan permease TYRxt + HEXT <-> TYR tat2 YDR046C BAP3 Branched chain amino acid permease TYRxt + HEXT <-> TYR bap3_7 YKR039W gapl General amino acid permease VALxt + HEXT <-> VAL gap20 YCL025C AGPI Amino acid permease for most neutral amino acids VALxt + HEXT <-> VAL gap31 YDR046C BAP3 Branched chain amino acid permease VALxt + HEXT <-> VAL bap3_8 YBR069C VAPI Amino acid permease VALxt + HEXT <-> VAL vap5 YBR068C BAP2 Branched chain amino acid permease VALxt + HEXT <-> VAL bap2 8 YFL055W AGP3 Amino acid permease for serine, aspartate, and SERxt + HEXT <-> SER agp3_1 glutamate YCL025C AGP1 Amino acid permease for most neutral amino acids SErxt + HEXT <-> SER gap27 YDR508C GNPI Glutamin permease (high affinity) SERxt + HEXT <-> SER gnp5 YKR039W gap1 General amino acid permease SERxt+ HEXT <-> SER gapl6 YPL265W DIP5 Dicarboxylic amino acid permease SERxt + HEXt <-> SER dip11 YBR069C TAT) Amino acid permease that transports valine, leucine, THRxt + HEXT <-> THR tat1_1 isleucine, tyrosine, tryptophan, and threonine YCL025C AGPI Amino acid permease for most neutral amino acids THRxt + HEXT <-> THR gap30 YKR039W gapl General amino acid permease THRxt + HEXT <-> THR gap17 YDR508C GNPI Glutamine permease (high affinity) THRxt+HEXT<->THR gnp6 YNL268W LYPI Lysine specific permease (high affinity) LYSxt + HEXT <-> LYS lyp1 YKR039W gapl General amino acid permease LYSxt + HEXT <-> LYS gapl2 <BR> <BR> YLL061W MMPI Highaffinity S-methylmethionine permease MMETxt+HEXT->MMET mmpl YPL274W SAM3 High affinity S-adenosylmethionine permease SAMxt + HEXT -> SAM sam3 YOR348C PUT4 Proline permease GABAxt + HEXT-> GABA put7 YDL210W uga4 Amino acid permease with high specificity for GABA GABAxt + HEXT -> GABA uga4 YBR132C AGP2 Plasma membrane camitine transporter CARxt <-> CAR agp2 YGL077C HNMI Cholinepermease CHOxt+HEXT-> MET hnml YNR056C B1OS transmembrane regulator of KAPA/DAPA transport BlOxt+HEXT->BIO bioSa YDL210W uga4 Amino acid permease with high specificity for GABA ALAVxt + HEXT-> ALAV uga5 YKR039W gap1 General amino acid permease ORNxt + HEXT <-> ORN gaplb YEL063C can I Permease for basic amino acids ORNxt + HEXT <-> ORN can I b Putrescine PTRSCxt + HEXT-> PTRSC ptrup Spermidine & putrescine SPRMDxt + HEXT-> SPRMD sprup I YKR093W PTR2 Dipeptide DIPEPxt+HEXT->DIPEP ptr2 YKR093W PTR2 Oligopeptide OPEPxt+HEXT->OPEP ptr3 YKR093W PTR2 Peptide PEPTxt + HEXT -> PEPT ptr4 YBR021W FUR4 Uracil URAxt+HEXT->URA uraupl Nicotinamide mononucleotide transporter NMNxt + HEXT -> NMN nmnup YER056C FCY2 Cytosine purine permease CYTSxt + HEXT-> CYTS fcy2_1 YER056C FCY2 Adenine ADxt+HEXT->AD fcy22 YER056C FCY2 Guanine GNxt + HEXT <-> GN fcy2_3 YER060W FCY21 Cytosine purine permease CYTSxt + HEXT -> CYTS fcy21_1 YER060W FCY21 Adenine ADxt + HEXT -> AD fcy21_2 YER060W FCY21 Guanine GNxt + HEXT <-> GN fcy21_3 YER060W-A FCY22 Cytosine purine permease CYTSxt + HEXT-> CYTS fcy22-1 YER060W-A FCY22 Adenine ADxt + HEXT -> AD fcy22_2 YER060W-A FCY22 Guanine GNxt + HEXT <-> GN fcy22_3 YGL186C YGL186 Cytosinepurinepermease CYTSxt+HEXT->CYTS cytupl C YGL186C YGL186 Adenine ADxt+HEXT->AD Adup1 C YGL186C YGL186 Guanine GNxt+HEXT<->GN gnup C G-system ADNxt + HEXT-> ADN ncgupl G-system GSNxt + HEXT-> GSN ncgup3 YBL042C FUII Uridinepermease, G-system URlxt+HEXT->URI uriup G-system CYTDxt + HEXT-> CYTD ncgup4 G-system (transports all nucleosides) INSxt + HEXT -> INS ncgup5 G-system XTSINExt + HEXT-> XTSINE ncgup6 G-system DTxt + HEXT-> DT ncgup7 G-system DINxt + HEXT-> DIN ncgup8 G-system DGxt + HEXT-> DG ncgup9 G-system DAxt + HEXT -> DA negup10 G-system DCxt + HEXT-> DC ncgup I I G-system DUxt + HEXT-> DU ncgup 12 C-system ADNxt+HEXT->ADN nccupl YBL042C FUII Uridine permease, C-system URIxt + HEXT -> URI nccup2 C-system CYTDxt + HEXT-> CYTD nccup3 C-system DTxt + HEXT -> DT nccup4 C-system DAxt + HEXT-> DA nccup5 C-system DCxt + HEXT-> DC nccup6 C-system DUxt + HEXT-> DU nccup7 Nucleosides and deoxynucleoside ADNxt + HEXT-> ADN ncup I Nucleosides and deoxynucleoside GSNxt + HEXT-> GSN ncup2 YBL042C FUII Uridinepermease, Nucleosidesanddeoxynucleoside URlxt+HEXT->URI ncup3 Nucleosides and deoxynucleoside CYTDxt + HEXT-> CYTD ncup4 Nucleosides and deoxynucleoside INSxt + HEXT-> W S ncup5 Nucleosides and deoxynucleoside DTxt + HEXT-> DT ncup7 Nucleosides and deoxynucleoside DINxt + HEXT-> DIN ncup8 Nucleosides and deoxynucleoside DGxt + HEXT-> DG ncup9 Nucleosides and deoxynucleoside DAxt + HEXT -> DA ncup10 Nucleosides and deoxynucleoside DCxt + HEXT-> DC ncup 11 Nucleosides and deoxynucleoside DUxt + HEXT-> DU ncup 12 Hypoxanthine HYXNxt + HEXT <-> HYXN hyxnup Xanthine XANxt <-> XAN xanup Metabolic By-Products YCR032W BPH I Probablc acetic acid export pump, Acetate transport ACxt + HEXT <-> AC acup Formate transport FORxt <-> FOR forup Ethanol transport ETHxt <-> ETH ethup Succinate transport SUCCxt + HEXT <-> SUCC succup YKL217W JEN1 Pyruvate lactate proton symport PYRxt + HEXT -> PYR jen1_1 Other Compounds YHL016C dur3 Urea active transport UREAxt+2HEXT<->UREA dur3 <BR> <BR> YGR121C MEPI Ammoniatransport NH3xt<->NH3 mepl YNL142W MEP2 Ammonia transport, low capacity high affinity NH3xt<->NH3 mep2 YPR138C MEP3 Ammonia transport, high capacity low affinity NH3xt <-> NH3 mep3 YJL129C trk1 Potassium transporter of the plasma membrane, high Kxt + HEXT <-> K trk I affinity, member of the potassium transporter (TRK) family of membrane transporters YBR294W SULI Sulfatepermease SLFxt->SLF sull YLR092W SUL2 Sulfate permease SLFxt -> SLF sul2 <BR> <BR> YGR125W YGR125 Sulfatepermease SLFxt->SLF sulup W YML123C pho84 inorganic phosphate transporter, transmembrane protein Plxt + HEXT <-> PI pho84 Citrate CITxt + HEXT <-> CIT citup Dicarboxylates FUMxt + HEXT <-> FUM fumup Fatty acid transport C140xt -> C140 faup1 Fatty acid transport C160xt-> C160 faup2 Fatty acid transport C 161 xt C 161 faup3 Fatty acid transport C180xt -> C180 faup4 Fatty acid transport C181xt -> C181 faup5 a-Ketoglutarate AKGxt + HEXT <-> AKG akgup YLR138W NHA1 Putative Na+/H+ antiporter NAxt <-> NA + HEXT nha1 YCR028C FEN2 Pantothcnate PNTOxt + HEXT <-> PNTO fen2 ATP drain flux for constant maintanencc requirements ATP-> ADP + Pl atpmt YCR024c-a PMPI H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmp1 YEL017c-a PMP2 H+-ATPase subunit, plasma membrane ATP -> ADP + PI + HEXT pmp2 YGLOO8c PMA1 H+-transporting P-type ATPase, major isoform, plasma ATP -> ADP + PI + HEXT pmal membrane YPL036w PMA2 H+-transporting P-type ATPase, minor isoform, plasma ATP -> ADP + PI + HEXT pma2 membrane Glyceraldehyde transport GLALxt <-> GLAL glaltx Acetaldehyde transport ACALxt <-> ACAL acaltx YLR237W TH17 Thiamine transport protein THMxt+HEXT->THLAMIN thm I YOR071C YOR071 Probable low affinity thiamine transporter THMxt + HEXT -> THIAMIN thm2 C YOR192C YOR192 Probable low affinity thiamine transporter THMxt+HEXT-> THIAMIN thm3 C <BR> <BR> YiR028W dal4 ATNxt-> ATN dal4 YJR152W dal5 ATTxt-> ATT dal5 MTHNxt <-> MTHN mthup PAPxt <-> PAP papx DTTPxt <-> DTTP dttpx THYxt <-> THY + HEXT thyx GA6Pxt <-> GA6P ga6pup YGR065C VHTI H+/biotin symporter and member of the allantoate BTxt + HEXT <-> BT btup permease family of the major facilitator superfamily AONAxt + HEXT <-> AONA kapaup DANNAxt + HEXT <-> DANNA dapaup OGTxt-> OGT ogtup SPRMxt-> SPRM sprmup PIMExt-> PIME pimeup Oxygen transport 02xt <-> 02 o2tx Carbon dioxide transport C02xt <-> C02 co2tx YOR011W AUS1 ERGOSTxt <-> ERGOST ergup YOR011W AUS1 Putative sterol transporter ZYMSTxt <-> ZYMST zymup RFLAVxt + HEXT-> RIBFLAV rflup [0055] Standard chemical names for the acronyms used to identify the reactants in the reactions of Table 2 are provided in Table 3.

TABLE 3 Abbreviation Metabolite 13GLUCAN l, 3-beta-D- Glucan 13PDG 3-Phospho-D- glyceryl phosphate 23DAACP 2, 3- Dehydroacyl- [acyl-carrier- protein] 23PDG 2, 3-Bisphospho- D-glycerate 2HDACP Hexadecenoyl- [acp] 2MANPD ("alpha"-D- mannosyl) (, 2)- "beta"-D- mannosyl- diacetylchitobio syldiphosphod olichol 2N6H 2-Nonaprenyl-6- hydroxyphenol 2NMHMB 3- Demethylubiqui none-9 2NMHMBm 3- Demethylubiqui none-9M 2NPMBm 2-Nonaprenyl-6- methoxy-1, 4- benzoquinoneM 2NPMMBm 2-Nonaprenyl-3- methyl-6- methoxy-1, 4- benzoquinoneM 2NPMP 2-Nonaprenyl-6- methoxyphenol 2NPMPm 2-Nonaprenyl-6- methoxyphenol M 2NPPP 2- Nonaprenylphen ol 2PG 2-Phospho-D- glycerate 3DDAH7P 2-Dehydro-3- deoxy-D- arabino- heptonate 7- phosphate 3HPACP (3R)-3- Hydroxypalmito yl- [acyl-carrier protein] 3PG 3-Phospho-D- glycerate 3PSER 3-Phosphoserine 3PSME 5-O-(1- Carboxyvinyl)- 3- phosphoshikima te 4HBZ 4- Hydroxybenzoat e 4HLT 4-Hydroxy-L- threonine 4HPP 3- (4- Hydroxyphenyl) pyruvate 4PPNCYS (R)-4'- Phosphopantoth enoyl-L-cysteine 4PPNTE Pantetheine 4'- phosphate 4PPNTEm Pantetheine 4'- phosphateM 4PPNTO D-4'- Phosphopantoth enate 5MTA 5'- Methylthioaden osine 6DGLC D-Gal alpha I- >6D-Glucose A6RP 5-Amino-6- ribitylamino-2, 4 (I H, 3 H)- pyrimidinedione A6RPSP 5-Amino-6- (5'- phosphoribosyla mino) uracil A6RP5P2 5-Amino-6- (5'- phosphoribityla mino) uracil AACCOA Acetoacetyl- CoA AACP Acyl-[acyl- carrier-protein] AATRE6P alpha, alpha'- Trehalose 6- phosphate ABUTm 2-Aceto-2- hydroxy butyrateM AC Acetate ACACP Acyl-[acyl- carrier protein] ACACPm Acyl-[acyl- carrier proteins ACAL Acetaldehyde ACALm AcetaldehydeM ACAR O- Acetylcamitine ACARm O- Acetylcamitine M ACCOA Acetyl-CoA ACCOAm Acetyl-CoAM ACLAC 2-Acetolactate ACLACm 2-AcetolactateM Acm Acetate ACNL 3- Indoleacetonitril e ACOA Acyl-CoA ACP Acyl-carrier protein ACPm Acyl-carrier proteinM ACTAC Acetoacetate ACTACm AcetoacetateM ACYBUT gamma-Amino- gamma- cyanobutanoate AD Adenine ADCHOR 4-amino-4- deoxychorismat e Adm AdenineM ADN Adenosine ADNm AdenonsineM ADP ADP ADPm ADPM ADPRIB ADPribose ADPRIBm ADPriboseM AGL3P Acyl-sn-glycerol 3-phosphate AHHMD 2-Amino-7,8- dihydro-4- hydroxy-6- (diphosphooxym ethyl) pteridine AHHMP 2-Amino-4- hydroxy-6- hydroxymethyl- 7, 8- dihydropteridine AHM 4-Amino-5- hydroxymethyl- 2- methylpyrimidin e AHMP 4-Amino-2- methyl-5- phosphomethylp yrimidine AHMPP 2-Methyl-4- amino-5- hydroxymethylp yrimidine diphosphate AHTD 2-Amino4- hydroxy-6- (erythro-1, 2, 3- trihydroxypropy l)- dihydropteridine triphosphate AICAR 1-(5'- Phosphoribosyl) - 5-amino-4- imidazolecarbox amide AIR Aminoimidazole ribotide AKA 2-Oxoadipate AKAm 2-OxoadipateM AKG 2-Oxoglutarate AKGm 2- OxoglutarateM AKP 2- Dehydropantoat e AKPm 2- Dehydropantoat eM ALA L-Alanine ALAGLY R-S- Alanylglycine ALAm L-AlanineM ALAV 5- Aminolevulinate ALAVm 5- Aminolevulinate M ALTRNA L-Arginyl- tRNA (Arg) AM6SA 2- Aminomuconate 6-semialdehyde AMA L-2- Aminoadipate AMASA L-2- Aminoadipate 6- semialdehyde AMG Methyl-D- glucoside AMP AMP AMPm AMPM AMUCO 2- Aminomuconate AN Anthranilate AONA 8-Amino-7- oxononanoate APEP Nalpha- Acetylpeptide APROA 3- Aminopropanal APROP alpha- Aminopropiono nitrile APRUT N- Acetylputrescine APS Adenylylsulfate ARAB D-Arabinose ARABLAC D-Arabinono- 1, 4-lactone ARG L-Arginine ARGSUCC N- (L- Arginino) succin ate ASER O-Acetyl-L- serine ASN L-Asparagine ASP L-Aspartate ASPERMD NI- Acetylspemidin c ASPm L-AspartateM ASPRM Ni- Acetylspermine ASPSA L-Aspartate 4- semialdehyde ASPTRNA L-Asparaginyl- tRNA (Asn) ASPTRNAm L-Asparaginyl- tRNA (Asn) M ASUC N6-(1, 2- Dicarboxyethyl) - AMP AT3P2 Acyldihydroxya cetone phosphate ATN Allantoin ATP ATP ATPm ATPM ATRNA tRNA (Arg) ATRP PI, P4-Bis (5'- adenosyl) tetraphosphate ATT Allantoate bALA beta-Alanine BASP 4-Phospho-L- aspartate bDG6P beta-D-Glucose 6-phosphate bDGLC beta-D-Glucose BIO Biotin BT Biotin C100ACP Decanoyl-[acp] C120ACP Dodecanoyl- acyl-carrier protein] C120ACPm Dodecanoyl- [acyl-carrier protein] M C140 |Myristic acid C140ACP Myristoyl- acyl- carrier protein] C140ACPm |Myristoyl-[acyl- carrier protein] M C141ACP Tetradecenoyl- [acyl-carrier proteins C141ACPm |tetradecenoyl- [acyl-carrier protein] M C160 Palmitate C 160ACP Hexadecanoyl- [acp] C160ACPm |Hexadecanoyl- [acp] M C161 |hexadecene C161ACP Palmitoyl- [acyl- carrier protein] C161ACPm Palmitoyl-[acyl- carrier protein] M C16A |C16_aldehydes 080 Stearate C180CAP |Stearoyl-[acyl- carrier protein] C180ACPm Stearoyl- [acyl- carrier protein] M C181 |I-Octadecene C181ACP Oleoyl- [acyl- carrier protein] C181ACPm Oleoyl- [acyl- carrier protein] M C182ACP |Linolenoyl- [acyl-carrier protein] C182ACPm |Linolenoyl- [acyl-carricr protein] M CAASP N-Carbamoyl-L- aspartate CAIR 1- (5-Phospho- D-ribosyl)-5- amino-4- imidazolecarbox ylate CALH 2- (3-Carboxy-3- aminopropyl)-L- histidine cAMP 3', 5'-Cyclic AMP CAP Carbamoyl phosphate CAR Camitine CARm CamitineM CBHCAP 3- Isopropylmalate CBHCAPm 3- Isopropylmalate M cCMP 3', 5'-Cyclic CMP cdAMP 3', 5'-Cyclic dAMP CDP CDP CDPCHO CDPcholine CDPDG CDPdiacylglyce rol CDPDGm CDPdiacylglyce rolM CDPETN CDPethanolami ne CER2 Ceramide-2 CER3 Ceramide-3 CGLY Cys-Gly cGMP 3', 5'-Cyclic GMP CHCOA 6- Carboxyhexano yl-CoA CHIT Chitin CHITO Chitosan CHO Choline CHOR Chorismate cIMP 3', 5'-Cyclic IMP CIT Citrate CITm Citrate CITR L-Citrulline CLm CardiolipinM CMP CMP CMPm CMPM CMUSA 2-Amino-3- carboxymuconat e semialdchyde C02 C02 C02m C02M COA CoA COAm CoAM CPAD5P 1- (2- Carboxyphenyla mino)-1-deoxy- D-ribulose 5- phosphate CPP Coproporphyrin ogen CTP CTP CTPm CTPM CYS L-Cysteine CYTD Cytidine CYTS Cytosine D45PI I-Phosphatidyl- D-myo-inositol 4, 5- bisphosphate D6PGC 6-Phospho-D- gluconate D6PGL D-Glucono-1, 5- lactone 6- phosphate D6RP5P 2, 5-Diamino-60 hydroxy-4- (5'- phosphoribosyla mino)- pyrimidine D8RL 6, 7-Dimethyl-8- (I-D- ribityl) lumazine DA Deoxyadenosine DADP dADP DAGLY Diacylglycerol DAMP dAMP dAMP dAMP DANNA 7, 8- Diaminononano ate DAPRP 1, 3- Diaminopropane DATP dATP DB4P L-3,4- Dihydroxy-2- butanon4- phosphate DC Deoxycytidine DCDP dCDP DCMP dCMP DCTP dCTP DFUC alpha-D- Fucoside DG Deoxyguanosine DGDP dGDP DGMP dGMP DGPP Diacylglycerol pyrophosphate DGTP dGTP DHF Dihydrofolate DHFm DihydrofolateM DHMVAm (R)-2, 3- dihydroxy-3- methylbutanoate M DHP 2-Amino-4- hydroxy-6- (D- erythro-1, 2,3- trihydroxypropy 1)-7, 8- dihydmpteridine DHPP Dihydroneopteri n phosphate DHPT Dihydropteroate DHSK 3- Dehydroshikima te DHSP Sphinganine i- phosphate DHSPIi 3- Dehydrosphinga nine DHVALm (R) -3-Hydroxy- 3-methyl-2- oxobutanoateM DIMGP D-erythro-l- (Imidazol-4- yl) glycerol 3- phosphate DIN Deoxyinosine DIPEP Dipeptide DISACIP 2, 3-bis (3- hydroxytetradec anoyl)-D- glucosaminyl- 1, 6-beta-D-2,3- bis (3- hydroxytetradec anoyl)-beta-D- glucosaminyl 1- phosphate DLIPOm Dihydrolipoami deM DMPP Dimethylallyl diphosphate DMZYMST 4, 4- Dimethylzymost erol DOL Dolichol DOLMANP Dolichyl beta- D-mannosyl phosphate DOLP Dolichyl phosphate DOLPP Dehydrodolichol diphosphate DOROA (S)- Dihydroorotate DPCOA Dephospho-CoA DPCOAm Dephospho- CoAM DPTH 2- [3-Carboxy-3- (methylammoni o) propyl]-L- histidine DQT 3- Dehydroquinate DRIP Deoxy-ribose 1- phosphate DR5P 2-Deoxy-D- ribose 5- phosphate DRIB Deoxyribosc DSAM S- Adenosyimethio ninamine DT Thymidine DTB Dethiobiotin DTBm DethiobiotinM DTDP dTDP DTMP dTMP DTP I-Deoxy-d- threo-2- pentose DTTP dTTP DU Deoxyuridine DUDP dUDP DUMP dUMP DUTP dUTP E4P D-Erythrose 4- phosphate EPM Epimelibiose EPST Episterol ER4P 4-Phospho-D- erythronate ERGOST Ergosterol ERTEOL Ergosta- 5, 7, 22, 24 (28)- tetraenol ERTROL Ergosta- 5, 7, 24 (28)- trienol ETH Ethanol ETHm EthanolM ETHM Ethanolamine FIP D-Fructose I- phosphate F26P D-Fructose 2, 6- bisphosphate F6P beta-D-Fructose 6-phosphate FAD FAD FADH2m FADH2M FADm FADM FALD Formaldehyde FDP beta-D-Fructose 1, 6- bisphosphate FERIm Ferricytochrome cM FEROm Ferrocytochrom ecM FEST Fecosterol FGAM 2-(Formamido)- N 1- (5'- phosphoribosyl) acetamidine FGAR 5'- Phosphoribosyl- N- formylglycinami de FGT S- Formylglutathio ne FKYN L- Formylkynureni ne FMN FMN FMNm FMNM FMRNAm N- Formylmethiony 1-tRNAM FOR Formate FORm Formate FPP trans, trans- Famesyl diphosphate FRU D-Fructose FTHF 10- Formyltetrahydr ofolate FTHFm 10- Formyltetrahydr ofolateM FUACAC 4- Fumarylacetoac etate FUC beta-D-Fucose FUM Fumarate FUMm FumarateM GIP |D-Glucose 1- phosphate G6P alpha-D- Glucose 6- phosphate GAIP D-Glucosamine I-phosphate GA6P D-Glucosamine 6-phosphate GABA 4- Aminobutanoate GABAL 4- Aminobutyralde hyde GABALm 4- Aminobutyralde hydeM GABAm 4- Aminobutanoate M GALIP D-Galactose I- phosphate GAR 5'- Phosphoribosylg lycinamide GBAD 4-Guanidino- butanamide GBAT 4-Guanidino- butanolate GC gamma-L- Glutamyl-L- cysteine GDP GDP GDPm GDPM GDPMAN GDPmannose GGL Galactosylglycer ol GL Glycerol GL3P sn-Glycerol 3- phosphate GL3Pm sn-Glycerol 3- phosphateM GLAC D-Galactose GLACL 1-alpha-D- Galactosyl-myo- inositol GLAL Glycolaldehyde GLAM Glucosamine GLC alpha-D- Glucose GLCN Gluconate GLN L-Glutamine GLP Glycylpeptide GLT L-Glucitol GLU L-Glutamate GLUGSAL L-Glutamate 5- semialdehyde GLUGSALm L-Glutamate 5- semialdehydeM GLUm Glutamate GLUP alpha-D- Glutamyl phosphate GLX Glyoxylate GLY Glycine GLYCOGEN Glycogen GLYm Glycine GLYN Glycerone GMP GMP GN Guanine GNm GuanineM GPP Geranyl diphosphate GSN Guanosine GSNm GuanosineM GTP GTP GTPm GTPM GTRNA L-Glutamyl- tRNA (Glu) GTRNAm L-Glutamyl- tRNA (Glu) M GTRP PI, P4-Bis (5'- guanosyl) tetraphosphate H202 H202 H2S Hydrogen sulfide H2SO3 Sulfite H3MCOA (S) -3-Hydroxy- 3- methylglutaryl- CoA H3MCOAm (S)-3-Hydroxy- 3- methylglutaryl- CoAM HACNm But-1-ene-1, 2, 4- tricarboxylateM HACOA (3S)-3- Hydroxyacyl- CoA HAN 3- Hydroxyanthran ilate HBA 4-Hydroxy- benzyl alcohol HCIT 2- Hydroxybutane- 1, 2, 4- tricarboxylate HCtTm 2- Hydroxybutane- 1, 2, 4- tricarboxylateM HCYS Homocysteine HEXT H+EXT HHTRNA L-Histidyl- tRNA (His) HIB (S) -3- Hydroxyisobuty rate HIBCOA (S) -3- Hydroxyisobuty ryl-CoA HICITm Homoisocitrate M HIS L-Histidine HISOL L-Histidinol HISOLP L-Histidinol phosphate HKYN 3- Hydroxykynure nine Hm H+M HMB Hydroxymethyl bilane HOMOGEN Homogentisate HPRO trans-4- Hydroxy-L- proline HSER L-Homoserine HTRNA tRNA (His) HYXAN Hypoxanthine IAC Indole-3-acetate IAD Indole-3- acetamide iBCOA 2- Methylpropanoy I-CoA ICIT Isocitrate ICITm |IsocitrateM IDP IDP IDPm IDPM IGP Indoleglycerol phosphate IGST 4,4- Dimethylcholest n-8,14,24-trienol IIMZYMST Intermediate M ethylzymosterol if IIZYMST Intermediate Zy mosterol_II- ILE L-Isoleucine ILEm L-IsoleucineM IMACP 3- (Imidazol-4- yl)-2-oxopropyl phosphate IMP IMP IMZYMST Intermediate M ethylymosterol _I INAC Indoleacetate INS Inosine IPC Inositol phosphorylcera mide IPPMAL 2- Isopropylmalate IPPMALm 2- Isopropylmalate M IPPP Isopentenyl diphosphate ISUCc a- Iminosuccinate ITCCOAm inaconyl-CoAM ITCm ItaconateM ITP ITP ITPm ITPM IVCOA 3- Methylbutanoyl- CoA IZYMST Intermediate_Zy mosterol_I K Potassium KYN L-Kynurenine LAC (R)-Lactate LACALm (S)- LactaldehydeM LACm (R)-LactateM LCCA a Long-chain carboxylic acid LEU L-Leucine LEUm L-LeucineM LGT (R)-S- Lactoylglutathio ne LGTm (R)-S- Lactoylglutathio neM UPtV 2, 3,2', 3'- tetrakis (3- hydroxytetradec anoyl)-D- glucosaminyl- 1, 6-beta-D- glucosamine 1, 4'- bisphosphate LIPOm LipoamideM LIPX Lipid X LLACm (S) -LactateM LLCT L-Cystathionine LLTRNA L-lysyl- tRNA (Lys) LLTRNAm L-lysyl- tRNA (Lys) M LNST Lanosterol LTRNA tRNA (Lys) LTRNAm tRNA (Lys) M LYS L-Lysine LYSm L-LysineM MAACOA a- Methylacetoacet yl-CoA MACAC 4- Maleylacetoacet ate MACOA 2-Methylprop-2- enoyl-CoA MAL Malate MALACP Malonyl-[acyl- carrier protein] MALACPm Malonyl-[acyl- carrier proteins MALCOA Malonyl-CoA MALm MalateM MALT Malonate MALTm Malonate MAN alpha-D- Mannose MAN I P alpha-D- Mannose 1- phosphate MAN2PD beta-D- Mannosyldiacet ylchitobiosyldip hosphodolichol MAN6P D-Mannose 6- phosphate MANNAN Mannan MBCOA Methylbutyryl- CoA MCCOA 2-Methylbut-2- enoyl-CoA MCRCOA 2-Methylbut-2- enoyl-CoA MDAP Meso- diaminopimelate MELI Melibiose MELT Melibiitol MET L-Methionine METH Methanethiol METHF 5, 10- Methenyltetrahy drofolate METHFm 5, 10- Methenyltetrahy drofolateM METTHF 5, 10- Methylenetetrah ydrofolate METTHFm 5, 10- Methylenetetrah ydrofolateM MGCOA 3- Methylglutacon yl-CoA MHIS N (pai)-Methyl- L-histidine MHVCOA a-Methyl-b- hydroxyvaleryl- CoA MI myo-Inositol MI1P 1L-myo-Inositol )-phosphate MIP2C Inositol- mannose-P- inositol-P- ceramide MIPC Mannose- inositol-P- ceramide MK Menaquinone MLT Maltose MMCOA Methylmalonyl- CoA MMET S- Methylmethioni ne MMS (S) - Methylmalonate semialdehyde MNT D-Mannitol MNT6P D-Mannitol 1- phosphate MTHF 5- Methyltetrahydr ofolate MTHFm 5- Methyltetrahydr ofolateM MTHGXL Methylglyoxal MTHN Methane MTHNm Methane MTHPTGLU 5- Methyltetrahydr opteroyltri-L- glutamate MTRNAm L-Methionyl- tRNAM MVL (R)-Mevalonate MVLm (R) - MevalonateM MYOI myo-Inositol MZYMST 4- Methylzymstero l N4HBZ 3-Nonaprenyl-4- hydroxybenzoat e NA Sodium NAAD Deamino-NAD+ NAADm Deamino- NAD+M NAC Nicotine NACm NicotinateM NAD NAD+ NADH NADH NADHm NADHM NADm NAD+M NADP NADP+ NADPH NADPH NADPHm NADPHM NADPm NADP+M NAG N- Acetylglucosami ne NAGA1P N-Acetyl-D- glucosamine 1- phosphate NAGA6P N-Acetyl-D- glucosamine 6- phosphate NAGLUm N-Acetyl-L- glutamateM NAGLUPm N-Acetyl-L- glutamate 5- phosphateM NAGLUSm N-Acetyl-L- glutamate 5- semialdehydeM NAM Nicotinamide NAMm NicotinamideM NAMN Nicotinate D ribonucleotide NAMNm Nicotinate D- ribonucleotideM NAORNm N2-Acetyl-L- omithineM NH3 NH3 NH3m NH3M NH4 NH4+ NPP all-trans- Nonaprenyl diphosphate NPPm all-trans- Nonaprenyl diphosphateM NPRAN N- (5-Phospho- D- ribosyl) anthranil ate 02 Oxygen 02m OxygenM OA Oxaloacetate OACOA 3-Oxoacyl-CoA OAHSER O-Acetyl-L- homoserine OAm OxaloacetateM OBUT 2-Oxobutanoate OBUTm 2- OxobutanoateM OFP Oxidized flavoprotein OGT Oxidized glutathione OHB 2-Oxo-3- hydroxy-4- phosphobutanoa te OHm HO-M OICAP 3-Carboxy-4- methyl-2- oxopentanoate OCAPm 3-Carboxy-4- methyl-2- oxopentanoateM OIVAL (R) -2- Oxoisovalerate OIVALm (R) -2- Oxoisovalerate M OMP Orotidine 5'- phosphate OMVAL 3-Methyl-2- oxobutanoate OMVALm 3-Methyl-2- oxobutanoateM OPEP Oligopeptide ORN L-Omithine ORNm L-OmithineM OROA Orotate OSLHSER O-Succinyl-L- homoserine OSUC Oxalosuccinate OSUCm Oxalosuccinate M OTHIO Oxidized thioredoxin OTHIOm Oxidized thioredoxinM OXA Oxaloglutarate OXAm Oxaloglutarate M P5C (S)-I-Pyrroline- 5-carboxylate P5Cm (S)-I-Pyrroline- 5-carboxylateM P5P Pyridoxine phosphate PA Phosphatidate PABA 4- Aminobenzoate PAC Phenylacetic acid PAD 2- Phenylacetamid e PALCOA Palmitoyl-CoA PAm PhosphatidateM PANT (R)-Pantoate PANTm (R)-PantoateM PAP Adenosine 3', 5'- bisphosphate PAPS 3'- Phosphoadenyly Isulfate PBG Porphobilinogen PC Phosphatidylcho line PC2 Sirohydrochlori n PCHO Choline phosphate PDLA Pyridoxamine PDLA5P Pyridoxamine phosphate PDME Phosphatidyl-N- dimethylethanol amine PE Phosphatidyleth anolamine PEm Phosphatidyleth anolamineM PEP Phosphoenolpyr uvate PEPD Peptide PEPm Phosphoenolpyr uvateM PEPT Peptide PETHM Ethanolamine phosphate PGm Phosphatidylgly cerolM PGPm Phosphatidylgly cerophosphateM PHC L-I-Pyrroline-3- hydroxy-5- carboxylate PHE L-Phenylalanine PHEN Prephenate PHP 3- Phosphonooxyp yruvate PHPYR Phenylpyruvate PHSER O-Phospho-L- homoserine PHSP Phytosphingosin et-phosphate PHT O-Phospho4- hydroxy-L- threonine PI Orthophosphate Plm Onhophosphate M PIME Pimelic Acid PINS 1-Phosphatidyl- D-myo-inositol PINS4P I-Phosphatidyl- I D-myo-inositol 4-phosphate PiNSP I-Phosphatidyl- I D-myo-inositol 3-phosphate PL Pyridoxal PL5P Pyridoxal phosphate PMME Phosphatidyl-N- methylethanola mine PMVL (R)-5- Phosphomevalo nate PNTO (R)- Pantothenate PPHG Protoporphyrino gen IX PPHGm Protopotphyrino gen KM PPI Pyrophosphate PPIm Pyrophosphate M PPtXm Protoporphyrin M PPMAL 2- Isopropylmaleat e PPMVL (R)-5- Diphosphomeva lonate PRAM 5- Phosphoribosyla mine PRBAMP N1- (5-Phospho- D-ribosyl)-AMP PRBATP N1- (5-Phospho- D-ribosyl)-ATP PRFICA 1- (5'- Phosphoribosyl) -5-formamido-4- imidazolecarbox amide PRFP 5-(5-Phospho- D- ribosylaminofor mimino)-I- (5- phosphoribosyl) -imidazole4- carboxamide PRLP N- (5'-Phospho- D-l'- ribulosylformim ino)-5-amino-1- (5"-phospho-D- ribosyl)-4- imidazolecarbox amide PRO L-Proline PROm L-ProlineM PROPCOA Propanoyl-CoA PRPP 5-Phospho- alpha-D-ribose I-diphosphate PRPPm 5-Phospho- alpha-D-ribose 1-diphosphateM PS Phosphatidylseri ne Mm Phosphatidylseri neM PSPH Phytosphingosin e PTHm HemeM PTRC Putrescine PTRSC Putreseine PUR15P |Pseudouridine 5'-phosphate PYR Pyruvate PYRDX Pyridoxine PYRm PyruvateM Q Ubiquinone-9 QA Pyridine-2,3- dicarboxylate QAm Pyridine-2, 3- dicarboxylateM QH2 |Ubiquinol QH2m UbiquinolM Qm Ubiquinone-9M RIP D-Ribose 1- phosphate R5P D-Ribose 5- phosphate RADP 4- (I-D- Ribitylamino)-5- amino-2, 6- dihydroxypyrimi dine RAF Raffinose RFP Reduced tlavoprotein RGT Glutathione RGTm GlutathioneM RIB D-Ribose R1BFLAVm RiboflavinM RBOFLAV Riboflavin RIPm alpha-D-Ribose 1-phosphateM RL5P D-Ribulose 5- phosphate RMN D-Rhamnose RTHIO Reduced thioredoxin RTHIOm Reduced thioredoxinM S Sulfur S17P |Sedoheptulose 1, 7- bisphosphate S23E (S) -2, 3- Epoxysqualene S7P Sedoheptulose 7-phosphate SACP N6- (L-1, 3- Dicarboxypropy I)-L-lysine SAH S-Adenosyl-L- homocysteine SAHm S-Adenosyl-L- homocysteineM SAICAR 1-(5'- Phosphoribosyl) -5-amino-4-(N- succinocarboxa mide)-imidazole SAM S-Adenosyl-L- methionine SAMm S-Adenosyl-L- methionineM SAMOB S-Adenosyl-4- methylthio-2- oxobutanoate SAPm S- Aminomethyldi hydrolipoylprote inM SER L-Serine SERm L-SerineM SLF Sulfate SLFm Sulfate SME Shikimate SME5P Shikimate 3- phosphate SOR Sorbose SCRIP Sorbose 1- phosphate SOT D-Sorbitol SPH Sphinganine SPMD Spermidine SPRM Spermine SPRMD Spermidine SQL Squalene SUC Sucrose SUCC Succinate SUCCm SuccinateM SUCCOAm Succinyl-CoAM SUCCSAL Succinate semialdehyde T3P1 D- Glyceraldehyde 3-phosphate T3P2 Glycerone phosphate T3P2m Glycerone phosphateM TAG16P |D-Tagatose 1, 6- bisphosphate TAG6P D-Tagatose 6- phosphate TAGLY Triacylglycerol TCOA Tetradecanoyl- CoA TGLP N- Tetradecanoylgl ycylpeptide THF Tetrahydrofolate THFG Tetrahydrofolyl- [Glu] (n) THFm Tetrahydrofolate M THIAMIN Thiamin THMP Thiamin monophosphate THPTGLU Tetrahydroptero yltri-L- glutamate THR L-Threonine THRm L-ThreonineM THY Thymine THZ 5- (2- Hydroxyethyl)- 4-methylthiazole THZP 4-Methyl-5- (2- phosphoethyl)- thiazole TPI D-myo-inositol 1, 4, 5- trisphosphate TPP Thiamin diphosphate TPPP Thiamin triphosphate TRE alpha, alpha- Trehalose TRE6P alpha, alpha'- Trehalose 6- phosphate TRNA tRNA TRNAG tRNA (Glu) TRNAGm tRNA (Glu) M TRNAm tRNAM TRP L-Tryptophan TRPm L-TryptophanM TRPTRNAm L-Tryptophanyl- tRNA (Trp) M TYR L-Tyrosine UDP UDP UDPG UDPglucose UDPG23A UDP-2, 3-bis (3- hydroxytetradec anoyl) glucosami ne UDPG2A UDP-3-0- (3- hydroxytetradec anoyl)-D- glucosamine UDPG2AA UDP-3-0- (3- hydroxytetradec anoyl)-N- acetylglucosami ne UDPGAL UDP-D- galactose UDPNAG UDP-N-acetyl- D-galactosamine UDPP Undecaprenyl diphosphate UGC (-)- Ureidoglycolate UMP UMP UPRG Uroporphyrinog en III URA Uracil UREA Urea UREAC Urgea-1- carboxylate URI Uridine UTP UTP VAL L-Valine X5P D-Xylose-5- phosphate XAN Xanthine XMP Xanthosine 5'- phosphate XTSINE Xanthine XTSN Xanthosine XUL D-Xylulose XYL D-Xylose ZYMST Zymosterol [0056] Depending upon the particular environmental conditions being tested and the desired activity, a reaction network data structure can contain smaller numbers of reactions such as at least 200,150, 100 or 50 reactions. A reaction network data structure having relatively few reactions can provide the advantage of reducing computation time and resources required to perform a simulation. When desired, a reaction network data structure having a particular subset of reactions can be made or used in which reactions that are not relevant to the particular simulation are omitted. Alternatively, larger numbers of reactions can be included in order to increase the accuracy or molecular detail of the methods of the invention or to suit a particular application. Thus, a reaction network data structure can contain at least 300,350, 400,450, 500,550, 600 or more reactions up to the number of reactions that occur in or by S. cerevisiae or that are desired to simulate the activity of the full set of reactions occurring in S. cerevisiae. A reaction network data structure that is substantially complete with respect to the metabolic reactions of S. cerevisiae provides the advantage of being relevant to a wide range of conditions to be simulated, whereas those with smaller numbers of metabolic reactions are limited to a particular subset of conditions to be simulated.

[0057] A S. cerevisiae reaction network data structure can include one or more reactions that occur in or by S. cerevisiae and that do not occur, either naturally or following manipulation, in or by another prokaryotic organism, such as Escherichia coli, Haemophilus influenzae, Bacillus subtilis, Helicobacter pylori or in or by another eukaryotic organism, such as Homo sapiens. Examples of reactions that are unique to S. cerevisiae compared at least to Escherichia coli, Haemophilus influenzae, and Helicobacter pylori include those identified in Table 4. It is understood that a S. cerevisiae reaction network data structure can also include one or more reactions that occur in another organism. Addition of such heterologous reactions to a reaction network data structure of the invention can be used in methods to predict the consequences of heterologous gene transfer in S. cerevisiae, for example, when designing or engineering man-made cells or strains.

Table 4. Reactions specific to S. cerevisiae metabolic network glk1_3, hxh1_1, hxk2_1, hxk1_4, hxk2_4, pfk1_3, idhl, idp1_1, idpl_2, idp2_1, idp3_1, idp2_2, idp3_2, Isc1R, pycl, pyc2, cyb2, dldl, ncpl, cytr, cyto, atpl, pmal, pma2, pmpl, pmp2, coxl, rbk1-2, achl_l, ach1_2, saf1_1R, unkrx11R, pdcl, pdc5, pdc6, lys20, adhlR, adh3R, adh2R, adh4R, adh5R, sfa1_2R, psal, pfk26, pfk27, fbp26, gal7R, mel1_2, mel1_3, me1_4R, mel1_5R, mel1_6R, mel1_7R, fsp2b, sorl, gsyl, gsy2, fksl, fks3, gsc2, tpsl, tps3, tsll, tps2, athl, nthl, nth2, fdhl, tfola, tfolb, durlR, dur2, nit2, cyrl, gukl_3R, ade2R, pdel, pde2_1, pde22, pde23, pde24, pde25, apa2, apal_l, apal_3, apal_2R, ura2_1, ura4R, ural_lR, uralOR, ura5R, ura3, npkR, furl, fcyl, tdkl, tdk2, urkl_l, urkl_2, urkl_3, deoalR, deoa2R, cddl_l, cdd1_2, cdc8r, dut1, cdc21, cmka2R, dcdlR, ura7_2, ura8_2, deg1R, pus1r, pus2R, pus4R, ura1_2R, ara1_1, ara1_2, gna1R, pcmlaR, qrilR, chsl, chs2, chs3, put2_1, put2, gltl, gdh2, cat2, yatl, mhtl, sam4, ecm402, cpa2, ura2_2, arg3, spe3, spe4, amd, amd2_1, atrna, msrl, rnas, ded81, hom6_1, cys4, glyl, agtR, gcv2R, sahl, met6, cys3, met17_1, metl7hR, dph5, met3, metl4, met17_2, met17_3, lys21, lys20a, lys3R, lys4R, lys12R, lys12bR, amitR, lys2-1, lys2_2, lys9R, lys1ar, krs1, msk1, pro2_1, gps1R, gps2R, pro33, pro3_4, pro3_1, pro3_5, dallR, dal2R, dal3R, his4_3, htsl, hmtl, tyrl, ctal, cttl, ald6, ald4_2, ald5_1, tdo2, kfor_, kynu_l, kmo, kynu_2, bnal, aaaa, aaab, aaac, tyrdega, tyrdegb, tyrdegc, trydegd, mswl, amd2_2, amd2_3, spra, sprb, sprc, sprd, spre, dysl, leu4, leul_2R, pclig, xapalR, xapa2R, xapa3R, ynkl_6R, ynkl_9R, udpR, pyrh1R, pyrh2R, cmpg, ushal, usha2, usha5, usha6, ushall, gpxIR, gpx2R, hyrlR, ecm38, nit2_1, nit2_2, nmtl, natl, nat2, bgl2, exgl, exg2, sprl, thi80_1, thi80 2, unkrxn8, phol 1, fmnll, fmnl 2, pdx3_2R, pdx3_3R, pdx3_4R, pdx3_1, pdx3_5, biol, foll_4, ftfa, ftfb, fol3R, met7R, rma1R, met12, met13, mis1_2, ade3_2, mtdl, fmtl, TypeIIl, TypeII2, TypeII4, TypeII3, TypeII6, TypeII5, TypeII_9, TypeII_8, TypeII_7, c100sn, c180sy, c182sy, faa1R, faa2R, faa3R, faa4R, fox2bR, pot1_1, erg10_1R, erg10_2R, Gat1_2, Gat2_2, ADHAPR, AGAT, slcl, Gatl_l, Gat2_1, cholaR, cholbR, cho2, opi3_1, opi3_2, cvki1, pct1, cpt1, ekil, ectl, eptlR, inol, impal, pisl, torl, tor2, vps34, pikl, sst4, fabl, mss4, plcl, pgslR, crdl, dppl, Ippl, hmgsR, hmglR, hmg2R, erg12_1, erg12_2, erg12_3, ergl24, erg8, mvdl, erg9, ergl, erg7, unkrxn3, unkrxn4, cdisoa, ergl 1_1, erg24, erg25_1, erg26_1, ergl 1_2, erg25_2, erg26_2, erg11_3, erg6, erg2, erg3, erg5, erg4, Icbl, 1cb2, tscl0, sur2, csyna, csynb, scs7, aurl, csg2, surl, iptl, lcb4_1, lcb5_1, lcb4_2, lcb5_2, lcb3, ysr3, dpll, sec59, dpml, pmtl, pmt2, pmt3, pmt4, pmt5, pmt6, kre2, ktrl, ktr2, ktr3, ktr4, ktr6, yurl, hor2, rhr2, cdal, cda2, daga, dakl, dak2, gpdl, nadglR, nadg2R, nptl, nadi, mnadphps, mnadglR, mnadg2R, mnptl, mnadi, heml, bet2, coql, coq2, coxlO, raml, rer2, srtl, mo2R, mco2R, methR, mmthnR, mnh3R, mthfR, mmthfR, mserR, mglyR, mcbhR, moicapR, mproR, mcmpR, macR, macar_, mcar_, maclacR, mactcR, moivalR, momvalR, mpmalRR, mslf, mthrR, maka, aacl, aac3, pet9, mirlaR, mirldR, dicl 2R, dic1_1R, dic1_3, mmltR, moabR, ctp1_1R, ctp1_2R, ctp1_3R, pyrcaR, mlacR, gcaR, gcb, ortlR, crcl, gut2, gpd2, mt3p, mgl3p, mfad, mriboR, mdtbR, mmcoaR, mmvlR, mpaR, mppntR, madR, mprppR, mdhfR, mqaR, moppR, msamR, msahR, sfcl, odclR, odc2R, hxtl_2, hxtlO_2, hxtl 1_2, hxtl3_2, hxtl 5_2, hxtl6_2, hxtl7_2, hxt2_2, hxt32, hxt42, hxt52, hxt62, hxt72, hxt85, hxt92, sucup, akmupR, sorupR, arbuplR, gltlupb, gal2_3, hxtl_l, hxtlO_l, hxtl 1, hxtl 1_1, hxtl3_1, hxtl5_1, hxtl6_1, hxt17_1, hxt2_1, hxt3_1, hxt4, hxt4-1, hxt5_1, hxt6 1, hxt7_1, hxt8_4, hxt9_1, stl1_1, gaupR, mmp1, mltup, mntup, nagup, rmnup, ribup, treup_2, treup_l, xylupR, uga5, bap2_lR, bap3_1R, gap5R, gnp3R, tat7R, vap7R, sam3, put7, uga4, dip9R, gap22R, gap7R, gnp1R, gap23R, gap9R, hiplR, vap6R, bap2_4R, bap3_4R, gap13R, gap26R, gnp4R, mup1R, mup3R, bap2_5R, bap3_5R, | gapl4R, gap29R, tat4R, ptrup, sprupl, ptr2, ptr3, ptr4, mnadd2, fcy2_3R, fcy21_3R, fcy22_3R, gnupR, hyxnupR, nccup3, nccup4, nccup6, nccup7, ncgup4, ncgup7, ncgup11, ncgup12, ncup4, ncup7, ncupll, ncupl2, ethupR, sull, sul2, sulup, citupR, amgupR, atpmt, glaltxR, dal4, dal5, mthupR, papxR, thyxR, ga6pupR, btupR, kapaupR, dapaupR, ogtup, sprmup, pimeup, thml, thm2, thm3, rflup, hnml, ergupR, zymupR, hxtl5, hxtl03, hxtl 1_3, hxt13_3, hxt15_3, hxtl63, hxtl73, hxt23, hxt33, hxt43, hxt53, hxt63, hxt73, hxt86, hxt93, itrl, itr2, bio5a, agp2R, dttpxR, gltup [0058] A reaction network data structure or index of reactions used in the data structure such as that available in a metabolic reaction database, as described above, can be annotated to include information about a particular reaction. A reaction can be annotated to indicate, for example, assignment of the reaction to a protein, macromolecule or enzyme that performs the reaction, assignment of a gene (s) that codes for the protein, macromolecule or enzyme, the Enzyme Commission (EC) number of the particular metabolic reaction or Gene Ontology (GO) number of the particular metabolic reaction, a subset of reactions to which the reaction belongs, citations to references from which information was obtained, or a level of confidence with which a reaction is believed to occur in S. cerevisiae. A computer readable medium or media of the invention can include a gene database containing annotated reactions. Such information can be obtained during the course of building a metabolic reaction database or model of the invention as described below.

[0059] As used herein, the term"gene database"is intended to mean a computer readable medium or media that contains at least one reaction that is annotated to assign a reaction to one or more macromolecules that perform the reaction or to assign one or more nucleic acid that encodes the one or more macromolecules that perform the reaction. A gene database can contain a plurality of reactions some or all of which are annotated. An annotation can include, for example, a name for a macromolecule; assignment of a function to a macromolecule; assignment of an organism that contains the macromolecule or produces the macromolecule; assignment of a subcellular location for the macromolecule; assignment of conditions under which a macromolecule is being expressed or being degraded; an amino acid or nucleotide sequence for the macromolecule; or any other annotation found for a macromolecule in a genome database such as those that can be found in Saccharomyces Genome Database maintained by Stanford University, or Comprehensive Yeast Genome Database maintained by MIPS.

[0060] A gene database of the invention can include a substantially complete collection of genes and/or open reading frames in S. cerevisiae or a substantially complete collection of the macromolecules encoded by the S. cerevisiae genome. Alternatively, a gene database can include a portion of genes or open reading frames in S. cerevisiae or a portion of the macromolecules encoded by the S. cerevisiae genome. The portion can be at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the genes or open reading frames encoded by the S. cerevisiae genome, or the macromolecules encoded therein. A gene database can also include macromolecules encoded by at least a portion of the nucleotide sequence for the S. cerevisiae genome such as at least 10%, 15%, 20%, 25%, 50%, 75%, 90% or 95% of the S. cerevisiae genome. Accordingly, a computer readable medium or media of the invention can include at least one reaction for each macromolecule encoded by a portion of the S. cerevisiae genome.

[0061] An in silico S. cerevisiae model according to the invention can be built by an iterative process which includes gathering information regarding particular reactions to be added to a model, representing the reactions in a reaction network data structure, and performing preliminary simulations wherein a set of constraints is placed on the reaction network and the output evaluated to identify errors in the network. Errors in the network such as gaps that lead to non-natural accumulation or consumption of a particular metabolite can be identified as described below and simulations repeated until a desired performance of the model is attained. An exemplary method for iterative model construction is provided in Example I.

[0062] Thus, the invention provides a method for making a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions in a computer readable medium or media. The method includes the steps of : (a) identifying a plurality of S. cerevisiae reactions and a plurality of S. cerevisiae reactants that are substrates and products of the S. cerevisiae reactions; (b) relating the plurality of S. cerevisiae reactants to the plurality of S. cerevisiae reactions in a data structure, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (c) making a constraint set for the plurality of S. cerevisiae reactions; (d) providing an objective function; (e) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, and (f) if at least one flux distribution is not predictive of S. cerevisiae physiology, then adding a reaction to or deleting a reaction from the data structure and repeating step (e), if at least one flux distribution is predictive of S. cerevisiae physiology, then storing the data structure in a computer readable medium or media.

[0063] Information to be included in a data structure of the invention can be gathered from a variety of sources including, for example, the scientific literature or an annotated genome sequence of S. cerevisiae such as the Genbank, a site maintained by the NCBI (ncbi. nlm. gov), the CYGD database, a site maintained by MIPS, or the SGD database, a site maintained by the School of Medicine at Stanford University, etc.

[0064] In the course of developing an in silico model of S. cerevisiae metabolism, the types of data that can be considered include, for example, biochemical information which is information related to the experimental characterization of a chemical reaction, often directly indicating a protein (s) associated with a reaction and the stoichiometry of the reaction or indirectly demonstrating the existence of a reaction occurring within a cellular extract; genetic information which is information related to the experimental identification and genetic characterization of a gene (s) shown to code for a particular protein (s) implicated in carrying out a biochemical event; genomic information which is information related to the identification of an open reading frame and functional assignment, through computational sequence analysis, that is then linked to a protein performing a biochemical event; physiological information which is information related to overall cellular physiology, fitness characteristics, substrate utilization, and phenotyping results, which provide evidence of the assimilation or dissimilation of a compound used to infer the presence of specific biochemical event (in particular translocations) ; and modeling information which is information generated through the course of simulating activity of S. cerevisiae using methods such as those described herein which lead to predictions regarding the status of a reaction such as whether or not the reaction is required to fulfill certain demands placed on a metabolic network.

[0065] The majority of the reactions occurring in S. cerevisiae reaction networks are catalyzed by enzymes/proteins, which are created through the transcription and translation of the genes found on the chromosome (s) in the cell. The remaining reactions occur through non-enzymatic processes. Furthermore, a reaction network data structure can contain reactions that add or delete steps to or from a particular reaction pathway. For example, reactions can be added to optimize or improve performance of a S. cerevisiae model in view of empirically observed activity. Alternatively, reactions can be deleted to remove intermediate steps in a pathway when the intermediate steps are not necessary to model flux through the pathway. For example, if a pathway contains 3 nonbranched steps, the reactions can be combined or added together to give a net reaction, thereby reducing memory required to store the reaction network data structure and the computational resources required for manipulation of the data structure. An example of a combined reaction is that for fatty acid degradation shown in Table 2, which combines the reactions for acyl-CoA oxidase, hydratase-dehydrogenase-epimerase, and acetyl-CoA C-acyltransferase of beta-oxidation of fatty acids.

[0066] The reactions that occur due to the activity of gene-encoded enzymes can be obtained from a genome database that lists genes or open reading frames identified from genome sequencing and subsequent genome annotation. Genome annotation consists of the locations of open reading frames and assignment of function from homology to other known genes or empirically determined activity. Such a genome database can be acquired through public or private databases containing annotated S. cerevisiae nucleic acid or protein sequences. If desired, a model developer can perform a network reconstruction and establish the model content associations between the genes, proteins, and reactions as described, for example, in Covert et al. Trends in Biochemical Sciences 26: 179-186 (2001) and Palsson, WO 00/46405.

[0067] As reactions are added to a reaction network data structure or metabolic reaction database, those having known or putative associations to the proteins/enzymes which enable/catalyze the reaction and the associated genes that code for these proteins can be identified by annotation. Accordingly, the appropriate associations for some or all of the reactions to their related proteins or genes or both can be assigned. These associations can be used to capture the non-linear relationship between the genes and proteins as well as between proteins and reactions. In some cases, one gene codes for one protein which then perform one reaction. However, often there are multiple genes which are required to create an active enzyme complex and often there are multiple reactions that can be carried out by one protein or multiple proteins that can carry out the same reaction. These associations capture the logic (i. e. AND or OR relationships) within the associations. Annotating a metabolic reaction database with these associations can allow the methods to be used to determine the effects of adding or eliminating a particular reaction not only at the reaction level, but at the genetic or protein level in the context of running a simulation or predicting S. cerevisiae activity.

[0068] A reaction network data structure of the invention can be used to determine the activity of one or more reactions in a plurality of S. cerevisiae reactions independent of any knowledge or annotation of the identity of the protein that performs the reaction or the gene encoding the protein. A model that is annotated with gene or protein identities can include reactions for which a protein or encoding gene is not assigned. While a large portion of the reactions in a cellular metabolic network are associated with genes in the organism's genome, there are also a substantial number of reactions included in a model for which there are no known genetic associations. Such reactions can be added to a reaction database based upon other information that is not necessarily related to genetics such as biochemical or cell based measurements or theoretical considerations based on observed biochemical or cellular activity. For example, there are many reactions that are not protein-enabled reactions.

Furthermore, the occurrence of a particular reaction in a cell for which no associated proteins or genetics have been currently identified can be indicated during the course of model building by the iterative model building methods of the invention.

[0069] The reactions in a reaction network data structure or reaction database can be assigned to subsystems by annotation, if desired. The reactions can be subdivided according to biological criteria, such as according to traditionally identified metabolic pathways (glycolysis, amino acid metabolism and the like) or according to mathematical or computational criteria that facilitate manipulation of a model that incorporates or manipulates the reactions. Methods and criteria for subdividing a reaction database are described in further detail in Schilling et al. , J. Theor. Biol. 203: 249-283 (2000). The use of subsystems can be advantageous for a number of analysis methods, such as extreme pathway analysis, and can make the management of model content easier. Although assigning reactions to subsystems can be achieved without affecting the use of the entire model for simulation, assigning reactions to subsystems can allow a user to search for reactions in a particular subsystem, which may be useful in performing various types of analyses. Therefore, a reaction network data structure can include any number of desired subsystems including, for example, 2 or more subsystems, 5 or more subsystems, 10 or more subsystems, 25 or more subsystems or 50 or more subsystems.

[0070] The reactions in a reaction network data structure or metabolic reaction database can be annotated with a value indicating the confidence with which the reaction is believed to occur in S. cerevisiae. The level of confidence can be, for example, a function of the amount and form of supporting data that is available. This data can come in various forms including published literature, documented experimental results, or results of computational analyses.

Furthermore, the data can provide direct or indirect evidence for the existence of a chemical reaction in a cell based on genetic, biochemical, and/or physiological data.

[0071] The invention further provides a computer readable medium, containing (a) a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product, and (b) a constraint set for the plurality of S. cerevisiae reactions.

[0072] Constraints can be placed on the value of any of the fluxes in the metabolic network using a constraint set. These constraints can be representative of a minimum or maximum allowable flux through a given reaction, possibly resulting from a limited amount of an enzyme present. Additionally, the constraints can determine the direction or reversibility of any of the reactions or transport fluxes in the reaction network data structure.

Based on the in vivo environment where S. cerevisiae lives the metabolic resources available to the cell for biosynthesis of essential molecules for can be determined. Allowing the corresponding transport fluxes to be active provides the in silico S. cerevisiae with inputs and outputs for substrates and by-products produced by the metabolic network.

[0073] Returning to the hypothetical reaction network shown in Figure 1, constraints can be placed on each reaction in the exemplary format, shown in Figure 3, as follows. The constraints are provided in a format that can be used to constrain the reactions of the stoichiometric matrix shown in Figure 2. The format for the constraints used for a matrix or in linear programming can be conveniently represented as a linear inequality such as ßj< Vj <aj : j = 1.... n (Eq. 1) where Vj is the metabolic flux vector, ßj is the minimum flux value and aj is the maximum flux value. Thus, aj can take on a finite value representing a maximum allowable flux through a given reaction or ßj can take on a finite value representing minimum allowable flux through a given reaction. Additionally, if one chooses to leave certain reversible reactions or transport fluxes to operate in a forward and reverse manner the flux may remain unconstrained by setting Pj to negative infinity and aj to positive infinity as shown for reaction R2 in Figure 3. If reactions proceed only in the forward reaction ßj is set to zero while aj is set to positive infinity as shown for reactions Rl, R3, R4, R5, and R6 in Figure 3. As an example, to simulate the event of a genetic deletion or non- expression of a particular protein, the flux through all of the corresponding metabolic reactions related to the gene or protein in question are reduced to zero by setting aj j and Pj to be zero. Furthermore, if one wishes to simulate the absence of a particular growth substrate, one can simply constrain the corresponding transport fluxes that allow the metabolite to enter the cell to be zero by setting aj j and (3j to be zero. On the other hand if a substrate is only allowed to enter or exit the cell via transport mechanisms, the corresponding fluxes can be properly constrained to reflect this scenario.

[0074] The in silico S. cerevisiae model and methods described herein can be implemented on any conventional host computer system, such as those based on Intel. RTM. microprocessors and running Microsoft Windows operating systems. Other systems, such as those using the UNIX or LINUX operating system and based on IBM. RTM. , DEC. RTM. or Motorola. RTM. microprocessors are also contemplated. The systems and methods described herein can also be implemented to run on client-server systems and wide-area networks, such as the Internet.

[0075] Software to implement a method or model of the invention can be written in any well-known computer language, such as Java, C, C++, Visual Basic, FORTRAN or COBOL and compiled using any well-known compatible compiler. The software of the invention normally runs from instructions stored in a memory on a host computer system. A memory or computer readable medium can be a hard disk, floppy disc, compact disc, magneto-optical disc, Random Access Memory, Read Only Memory or Flash Memory. The memory or computer readable medium used in the invention can be contained within a single computer or distributed in a network. A network can be any of a number of conventional network systems known in the art such as a local area network (LAN) or a wide area network (WAN).

Client-server environments, database servers and networks that can be used in the invention are well known in the art. For example, the database server can run on an operating system such as UNIX, running a relational database management system, a World Wide Web application and a World Wide Web server. Other types of memories and computer readable media are also contemplated to function within the scope of the invention.

[0076] A database or data structure of the invention can be represented in a markup language format including, for example, Standard Generalized Markup Language (SGML), Hypertext markup language (HTML) or Extensible Markup language (XML). Markup languages can be used to tag the information stored in a database or data structure of the invention, thereby providing convenient annotation and transfer of data between databases and data structures. In particular, an XML format can be useful for structuring the data representation of reactions, reactants and their annotations; for exchanging database contents, for example, over a network or internet; for updating individual elements using the document object model; or for providing differential access to multiple users for different information content of a data base or data structure of the invention. XML programming methods and editors for writing XML code are known in the art as described, for example, in Ray, Learning XML O'Reilly and Associates, Sebastopol, CA (2001).

[0077] A set of constraints can be applied to a reaction network data structure to simulate the flux of mass through the reaction network under a particular set of environmental conditions specified by a constraints set. Because the time constants characterizing metabolic transients and/or metabolic reactions are typically very rapid, on the order of milli-seconds to seconds, compared to the time constants of cell growth on the order of hours to days, the transient mass balances can be simplified to only consider the steady state behavior.

Referring now to an example where the reaction network data structure is a stoichiometric matrix, the steady state mass balances can be applied using the following system of linear equations Sev=0 (Eq. 2) where S is the stoichiometric matrix as defined above and v is the flux vector. This equation defines the mass, energy, and redox potential constraints placed on the metabolic network as a result of stoichiometry. Together Equations 1 and 2 representing the reaction constraints and mass balances, respectively, effectively define the capabilities and constraints of the metabolic genotype and the organism's metabolic potential. All vectors, v, that satisfy Equation 2 are said to occur in the mathematical nullspace of S. Thus, the null space defines steady-state metabolic flux distributions that do not violate the mass, energy, or redox balance constraints. Typically, the number of fluxes is greater than the number of mass balance constraints, thus a plurality of flux distributions satisfy the mass balance constraints and occupy the null space. The null space, which defines the feasible set of metabolic flux distributions, is further reduced in size by applying the reaction constraints set forth in Equation 1 leading to a defined solution space. A point in this space represents a flux distribution and hence a metabolic phenotype for the network. An optimal solution within the set of all solutions can be determined using mathematical optimization methods when provided with a stated objective and a constraint set. The calculation of any solution constitutes a simulation of the model.

[0078] Objectives for activity of S. cerevisiae can be chosen to explore the improved use of the metabolic network within a given reaction network data structure. These objectives can be design objectives for a strain, exploitation of the metabolic capabilities of a genotype, or physiologically meaningful objective functions, such as maximum cellular growth.

Growth can be defined in terms of biosynthetic requirements based on literature values of biomass composition or experimentally determined values such as those obtained as described above. Thus, biomass generation can be defined as an exchange reaction that removes intermediate metabolites in the appropriate ratios and represented as an objective function. In addition to draining intermediate metabolites this reaction flux can be formed to utilize energy molecules such as ATP, NADH and NADPH so as to incorporate any growth dependent maintenance requirement that must be met. This new reaction flux then becomes another constraint/balance equation that the system must satisfy as the objective function.

Using the stoichiometric matrix of Figure 2 as an example, adding such a constraint is analogous to adding the additional column Vgoth to the stoichiometric matrix to represent fluxes to describe the production demands placed on the metabolic system. Setting this new flux as the objective function and asking the system to maximize the value of this flux for a given set of constraints on all the other fluxes is then a method to simulate the growth of the organism.

[0079] Continuing with the example of the stoichiometric matrix applying a constraint set to a reaction network data structure can be illustrated as follows. The solution to equation 2 can be formulated as an optimization problem, in which the flux distribution that minimizes a particular objective is found. Mathematically, this optimization problem can be stated as: Minimize Z (Eq. 3) where (Eq. 4) z = yci'Vi where Z is the objective which is represented as a linear combination of metabolic fluxes v ; using the weights ci in this linear combination. The optimization problem can also be stated as the equivalent maximization problem; i. e. by changing the sign on Z. Any commands for solving the optimization problem can be used including, for example, linear programming commands.

[0080] A computer system of the invention can further include a user interface capable of receiving a representation of one or more reactions. A user interface of the invention can also be capable of sending at least one command for modifying the data structure, the constraint set or the commands for applying the constraint set to the data representation, or a combination thereof. The interface can be a graphic user interface having graphical means for making selections such as menus or dialog boxes. The interface can be arranged with layered screens accessible by making selections from a main screen. The user interface can provide access to other databases useful in the invention such as a metabolic reaction database or links to other databases having information relevant to the reactions or reactants in the reaction network data structure or to S. cerevisiae physiology. Also, the user interface can display a graphical representation of a reaction network or the results of a simulation using a model of the invention.

[0081] Once an initial reaction network data structure and set of constraints has been created, this model can be tested by preliminary simulation. During preliminary simulation, gaps in the network or"dead-ends"in which a metabolite can be produced but not consumed or where a metabolite can be consumed but not produced can be identified. Based on the results of preliminary simulations areas of the metabolic reconstruction that require an additional reaction can be identified. The determination of these gaps can be readily calculated through appropriate queries of the reaction network data structure and need not require the use of simulation strategies, however, simulation would be an alternative approach to locating such gaps.

[0082] In the preliminary simulation testing and model content refinement stage the existing model is subjected to a series of functional tests to determine if it can perform basic requirements such as the ability to produce the required biomass constituents and generate predictions concerning the basic physiological characteristics of the particular organism strain being modeled. The more preliminary testing that is conducted the higher the quality of the model that will be generated. Typically the majority of the simulations used in this stage of development will be single optimizations. A single optimization can be used to calculate a single flux distribution demonstrating how metabolic resources are routed determined from the solution to one optimization problem. An optimization problem can be solved using linear programming as demonstrated in the Examples below. The result can be viewed as a display of a flux distribution on a reaction map. Temporary reactions can be added to the network to determine if they should be included into the model based on modeling/simulation requirements.

[0083] Once a model of the invention is sufficiently complete with respect to the content of the reaction network data structure according to the criteria set forth above, the model can be used to simulate activity of one or more reactions in a reaction network. The results of a simulation can be displayed in a variety of formats including, for example, a table, graph, reaction network, flux distribution map or a phenotypic phase plane graph.

[0084] Thus, the invention provides a method for predicting a S. cerevisiae physiological function. The method includes the steps of (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function, and (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function.

[0085] As used herein, the term"physiological function, "when used in reference to S. cerevisiae, is intended to mean an activity of a S. cerevisiae cell as a whole. An activity included in the term can be the magnitude or rate of a change from an initial state of a S. cerevisiae cell to a final state of the S. cerevisiae cell. An activity can be measured qualitatively or quantitatively. An activity included in the term can be, for example, growth, energy production, redox equivalent production, biomass production, development, or consumption of carbon, nitrogen, sulfur, phosphate, hydrogen or oxygen. An activity can also be an output of a particular reaction that is determined or predicted in the context of substantially all of the reactions that affect the particular reaction in a S. cerevisiae cell or substantially all of the reactions that occur in a S. cerevisiae cell. Examples of a particular reaction included in the term are production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, or transport of a metabolite. A physiological function can include an emergent property which emerges from the whole but not from the sum of parts where the parts are observed in isolation (see for example, Palsson Nat. Biotech 18: 1147-1150 (2000)).

[0086] A physiological function of S. cerevisiae reactions can be determined using phase plane analysis of flux distributions. Phase planes are representations of the feasible set which can be presented in two or three dimensions. As an example, two parameters that describe the growth conditions such as substrate and oxygen uptake rates can be defined as two axes of a two-dimensional space. The optimal flux distribution can be calculated from a reaction network data structure and a set of constraints as set forth above for all points in this plane by repeatedly solving the linear programming problem while adjusting the exchange fluxes defining the two-dimensional space. A finite number of qualitatively different metabolic pathway utilization patterns can be identified in such a plane, and lines can be drawn to demarcate these regions. The demarcations defining the regions can be determined using shadow prices of linear optimization as described, for example in Chvatal, Linear Programming New York, W. H. Freeman and Co. (1983). The regions are referred to as regions of constant shadow price structure. The shadow prices define the intrinsic value of each reactant toward the objective function as a number that is either negative, zero, or positive and are graphed according to the uptake rates represented by the x and y axes. When the shadow prices become zero as the value of the uptake rates are changed there is a qualitative shift in the optimal reaction network.

[0087] One demarcation line in the phenotype phase plane is defined as the line of optimality (LO). This line represents the optimal relation between respective metabolic fluxes. The LO can be identified by varying the x-axis flux and calculating the optimal y- axis flux with the objective function defined as the growth flux. From the phenotype phase plane analysis the conditions under which a desired activity is optimal can be determined.

The maximal uptake rates lead to the definition of a finite area of the plot that is the predicted outcome of a reaction network within the environmental conditions represented by the constraint set. Similar analyses can be performed in multiple dimensions where each dimension on the plot corresponds to a different uptake rate. These and other methods for using phase plane analysis, such as those described in Edwards et al. , Biotech Bioeng. 77: 27- 36 (2002), can be used to analyze the results of a simulation using an in silico S. cerevisiae model of the invention.

[0088] A physiological function of S. cerevisiae can also be determined using a reaction map to display a flux distribution. A reaction map of S. cerevisiae can be used to view reaction networks at a variety of levels. In the case of a cellular metabolic reaction network a reaction map can contain the entire reaction complement representing a global perspective.

Alternatively, a reaction map can focus on a particular region of metabolism such as a region corresponding to a reaction subsystem described above or even on an individual pathway or reaction. An example of a reaction map showing a subset of reactions in a reaction network of S. cerevisiae is shown in Figure 4.

(0089] The invention also provides an apparatus that produces a representation of a S. cerevisiae physiological function, wherein the representation is produced by a process including the steps of : (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) providing an objective function; (d) determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data structure, thereby predicting a S. cerevisiae physiological function, and (e) producing a representation of the activity of the one or more S. cerevisiae reactions.

[0090] The methods of the invention can be used to determine the activity of a plurality of S. cerevisiae reactions including, for example, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, transport of a metabolite and metabolism of an alternative carbon source. In addition, the methods can be used to determine the activity of one or more of the reactions described above or listed in Table 2.

[0091] The methods of the invention can be used to determine a phenotype of a S. cerevisiae mutant. The activity of one or more S. cerevisiae reactions can be determined using the methods described above, wherein the reaction network data structure lacks one or more gene-associated reactions that occur in S. cerevisiae. Alternatively, the methods can be used to determine the activity of one or more S. cerevisiae reactions when a reaction that does not naturally occur in S. cerevisiae is added to the reaction network data structure. Deletion of a gene can also be represented in a model of the invention by constraining the flux through the reaction to zero, thereby allowing the reaction to remain within the data structure. Thus, simulations can be made to predict the effects of adding or removing genes to or from S. cerevisiae. The methods can be particularly useful for determining the effects of adding or deleting a gene that encodes for a gene product that performs a reaction in a peripheral metabolic pathway.

[0092] A drug target or target for any other agent that affects S. cerevisiae function can be predicted using the methods of the invention. Such predictions can be made by removing a reaction to simulate total inhibition or prevention by a drug or agent. Alternatively, partial inhibition or reduction in the activity a particular reaction can be predicted by performing the methods with altered constraints. For example, reduced activity can be introduced into a model of the invention by altering the aj or pu values for the metabolic flux vector of a target reaction to reflect a finite maximum or minimum flux value corresponding to the level of inhibition. Similarly, the effects of activating a reaction, by initiating or increasing the activity of the reaction, can be predicted by performing the methods with a reaction network data structure lacking a particular reaction or by altering the aj or ßj values for the metabolic flux vector of a target reaction to reflect a maximum or minimum flux value corresponding to the level of activation. The methods can be particularly useful for identifying a target in a peripheral metabolic pathway.

[0093] Once a reaction has been identified for which activation or inhibition produces a desired effect on S. cerevisiae function, an enzyme or macromolecule that performs the reaction in S. cerevisiae or a gene that expresses the enzyme or macromolecule can be identified as a target for a drug or other agent. A candidate compound for a target identified by the methods of the invention can be isolated or synthesized using known methods. Such methods for isolating or synthesizing compounds can include, for example, rational design based on known properties of the target (see, for example, DeCamp et al. , Protein Engineering Principles and Practice, Ed. Cleland and Craik, Wiley-Liss, New York, pp. 467- 506 (1996) ), screening the target against combinatorial libraries of compounds (see for example, Houghten et al., Nature, 354,84-86 (1991) ; Dooley et al., Science, 266,2019-2022 (1994), which describe an iterative approach, or R. Houghten et al. PCT/US91/08694 and U. S. Patent 5,556, 762 which describe a positional-scanning approach), or a combination of both to obtain focused libraries. Those skilled in the art will know or will be able to routinely determine assay conditions to be used in a screen based on properties of the target or activity assays known in the art.

[0094] A candidate drug or agent, whether identified by the methods described above or by other methods known in the art, can be validated using an in silico S. cerevisiae model or method of the invention. The effect of a candidate drug or agent on S. cerevisiae physiological function can be predicted based on the activity for a target in the presence of the candidate drug or agent measured in vitro or in vivo. This activity can be represented in an in silico S. cerevisiae model by adding a reaction to the model, removing a reaction from the model or adjusting a constraint for a reaction in the model to reflect the measured effect of the candidate drug or agent on the activity of the reaction. By running a simulation under these conditions the holistic effect of the candidate drug or agent on S. cerevisiae physiological function can be predicted.

[0095] The methods of the invention can be used to determine the effects of one or more environmental components or conditions on an activity of S. cerevisiae. As set forth above, an exchange reaction can be added to a reaction network data structure corresponding to uptake of an environmental component, release of a component to the environment, or other environmental demand. The effect of the environmental component or condition can be further investigated by running simulations with adjusted aj or ßj values for the metabolic flux vector of the exchange reaction target reaction to reflect a finite maximum or minimum flux value corresponding to the effect of the environmental component or condition. The environmental component can be, for example an alternative carbon source or a metabolite that when added to the environment of S. cerevisiae can be taken up and metabolized. The environmental component can also be a combination of components present for example in a minimal medium composition. Thus, the methods can be used to determine an optimal or minimal medium composition that is capable of supporting a particular activity of S. cerevisiae.

[0096] The invention further provides a method for determining a set of environmental components to achieve a desired activity for S. cerevisiae. The method includes the steps of (a) providing a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, wherein each of the S. cerevisiae reactions includes a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating the substrate and the product; (b) providing a constraint set for the plurality of S. cerevisiae reactions; (c) applying the constraint set to the data representation, thereby determining the activity of one or more S. cerevisiae reactions (d) determining the activity of one or more S. cerevisiae reactions according to steps (a) through (c), wherein the constraint set includes an upper or lower bound on the amount of an environmental component and (e) repeating steps (a) through (c) with a changed constraint set, wherein the activity determined in step (e) is improved compared to the activity determined in step (d).

[0097] The following examples are intended to illustrate but not limit the present invention.

EXAMPLE I Reconstruction of the metabolic network of S. cerevisiae [0098] This example shows how the metabolic network of S. cerevisiae can be reconstructed.

[0099] The reconstruction process was based on a comprehensive search of the current knowledge of metabolism in S. cerevisiae as shown in Figure 5. A reaction database was built using the available genomic and metabolic information on the presence, reversibility, localization and cofactor requirements of all known reactions. Furthermore, information on non-growth-dependent and growth-dependent ATP requirements and on the biomass composition was used.

[0100] For this purpose different online reaction databases, recent publications and review papers (Table 5 and 9), and established biochemistry textbooks (Zubay, Biochemistry Wm. C.

Brown Publishers, Dubuque, IA (1998); Stryer, Biochemistry W. H. Freeman, New York, NY (1988) ) were consulted. Information on housekeeping genes of S. cerevisiae and their functions were taken from three main yeast on-line resources: The MIPS Comprehensive Yeast Genome Database (CYGD) (Mewes et al., Nucleic Acids Research 30 (1) : 31-34 (2002) ) ; The Saccharomyces Genome Database (SGD) (Cherry et al. , Nucleic Acids Research 26 (1) : 73-9 (1998) ) ; The Yeast Proteome Database (YPD) (Costanzo et al. , Nucleic Acids Research 29 (1) : 75-9 (2001)).

[0101] The following metabolic maps and protein databases (available online) were investigated: Kyoto Encyclopedia of Genes and Genomes database (KEGG) (Kanehisa et al., Nucleic Acids Research 28 (1) : 27-30 (2000) ) ; The Biochemical Pathways database of the Expert Protein Analysis System database (ExPASy) (Appel et al. , Trends Biochem Sci. 19 (6): 258-260 (1994) ) ; ERGO from Integrated Genomics (www. integratedgenomics. com) SWISS-PROT Protein Sequence database (Bairoch et al., Nucleic Acids Research 28 (1) : 45-48 (2000)).

[0102] Table 5 lists additional key references that were consulted for the reconstruction of the metabolic network of S. cerevisiae.

Table 5 Amino acid biosynthesis Strathem et al. , The Molecular biology of the yeast Saccharomyces : metabolism and gene expression Cold Spring Harbor Laboratory, Cold Spring Harbor, N. Y. (1982)) Lipid synthesis Daumetal., Yeast 14 (16): 1471-510 (1998); Dickinson et al., The metabolism and molecular physiology of Saccharomyces cerevisiae Taylor & Francis, London; Philadelphia (1999); Dickson et al., Methods Enzyme. 311: 3-9 (2000); Dickson, Annu Rev Biochem 67: 27-48 (1998); Parks, CRC Crit Rev Microbiol 6 (4): 301-41 (1978)) Nucleotide Metabolism Strathem et al. , supara (1982)) Oxidative phosphorylation and electron transport (Verduyn et al. , Antonie Van Leeuwenhoek 59 (1) : 49-63 (1991); Overkamp et al. , J. of Bacteriol 182 (10): 2823-2830 (2000)) Primary Metabolism Zimmerman et al. , Yeast sugar metabolism: biochemistry, genetics, biotechnology, and applications Technomic Pub. , Lancaster, PA (1997); Dickinson et al. , supra (1999); Strathem et al., supra (1982) ) Transport across the cytoplasmic membrane Paulsen et al. , FEBS Lett 430 (1-2): 116-125 (1998); Wieczorke et al., FEBS Lett 464 (3): 123-128 (1999); Regenberg et al. , Curr Genet 36 (6): 317-328 (1999); Andre, Yeast 11 (16): 1575-1611 (1995)) Transport across the mitochondrial membrane Palmieri et al. , J Bioenerg Biomembr 32 (1) : 67: 77 (2000); Palmieri et al. , Biochim Biophys Acta 1459 (2-3): 363-369 (2000); Palmieri et al. , J Biol Chem 274 (32): 22184-22190 (1999); Palmieri et al. , FEBS Lett 417 (1) : 114-118 (1997); Paulsen et al., supra (1998); Pallotta et al. , FEBS Lett 428 (3): 245-249 (1998); Tzagologg et al. Mitochondria Plenum Press, New York (1982); Andre Yeast 11 (16): 1575-611 (1995)) [0103] All reactions are localized into the two main compartments, cytosol and mitochondria, as most of the common metabolic reactions in S. cerevisiae take place in these compartments. Optionally, one or more additional compartments can be considered.

Reactions located in vivo in other compartments or reactions for which no information was available regarding localization were assumed to be cytosol. All corresponding metabolites were assigned appropriate localization and a link between cytosol and mitochondria was established through either known transport and shuttle systems or through inferred reactions to meet metabolic demands.

[0104] After the initial assembly of all the metabolic reactions the list was manually examined for resolution of detailed biochemical issues. A large number of reactions involve cofactors utilization, and for many of these reactions the cofactor requirements have not yet been completely elucidated. For example, it is not clear whether certain reactions use only NADH or only NADPH as a cofactor or can use both cofactors, whereas other reactions are known to use both cofactors. For example, a mitochondrial aldehyde dehydrogenase encoded by ALD4 can use both NADH and NADPH as a cofactor (Remize et al. Appl Environ Microbiol 66 (8): 3151-3159 (2000) ). In such cases, two reactions are included in the reconstructed metabolic network.

[0105] Further considerations were taken into account to preserve the unique features of S. cerevisiae metabolism. S. cerevisiae lacks a gene that encodes the enzyme transhydrogenase.

Insertion of a corresponding gene from Azetobacter vinelandii in S. cerevisiae has a major impact on its phenotypic behavior, especially under anaerobic conditions (Niessen et al.

Yeast 18 (1) : 19-32 (2001) ). As a result, reactions that create a net transhydrogenic effect in the model were either constrained to zero or forced to become irreversible. For instance, the flux carried by NADH dependent glutamate dehydrogenase (Gdh2p) was constrained to zero to avoid the appearance of a net transhydrogenase activity through coupling with the NADPH dependent glutamate dehydrogenases (Gdhlp and Gdh3p).

[0106] Once a first generation model is prepared, microbial behavior can be modeled for a specific scenario, such as anaerobic or aerobic growth in continuous cultivation using glucose as a sole carbon source. Modeling results can then be compared to experimental results. If modeling and experimental results are in agreement, the model can be considered as correct, and it is used for further modeling and predicting S. cerevisiae behavior. If the modeling and experimental results are not in agreement, the model has to be evaluated and the reconstruction process refined to determine missing or incorrect reactions, until modeling and experimental results are in agreement. This iterative process is shown in Figure 5 and exemplified below.

EXAMPLE II Calculation of the P/O ratio [0107] This example shows how the genome-scale reconstructed metabolic model of S. cerevisiae was used to calculate the P/O ratio, which measures the efficiency of aerobic respiration. The P/O ratio is the number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS).

[0108] Linear optimization was applied, and the in silico P/O ratio was calculated by first determining the maximum number of ATP molecules produced per molecule of glucose through the electron transport system (ETS), and then interpolating the in silico P/O ratio using the theoretical relation (i. e. in S. cerevisiae for the P/O ratio of 1.5, 18 ATP molecules are produced).

[0109] Experimental studies of isolated mitochondria have shown that S. cerevisiae lacks site I proton translocation (Verduyn et al. , Antonie Van Leeuwenhoek 59 (1) : 49-63 (1991)).

Consequently, estimation of the maximum theoretical or"mechanistic"yield of the ETS alone gives a P/O ratio of 1.5 for oxidation of NADH in S. cerevisiae grown on glucose (Verduyn et al. , supra (1991)). However, based on experimental measurements, it has been determined that the net in vivo P/O ratio is approximately 0.95 (Verduyn et al. , supra (1991)).

This difference is generally thought to be due to the use of the mitochondrial transmembrane proton gradient needed to drive metabolite exchange, such as the proton-coupled translocation of pyruvate, across the inner mitochondrial membrane. Although simple diffusion of protons (or proton leakage) would be surprising given the low solubility of protons in the lipid bilayer, proton leakage is considered to contribute to the lowered P/O ratio due to the relatively high electrochemical gradient across the inner mitochondrial membrane (Westerhoff and van Dam, Thermodynamics and control of biological free-energy transduction Elsevier, New York, NY (1987)).

[0110] Using the reconstructed network, the P/O ratio was calculated to be 1.04 for oxidation of NADH for growth on glucose by first using the model to determine the maximum number of ATP molecules produced per molecule of glucose through the electron transport system (ETS) (YATP, max=12.5 ATP molecules/glucose molecule via ETS in silico). The in silico P/O ratio was then interpolated using the theoretical relation (i. e. 18 ATP molecules per glucose molecule are produced theoretically when the P/O ratio is 1.5).

The calculated P/O ratio was found to be close to the experimentally determined value of 0.95. Proton leakage, however, was not included in the model, which suggests that the major reason for the lowered P/O ratio is the use of the proton gradient for solute transport across the inner mitochondrial membrane. This result illustrates the importance of including the complete metabolic network in the analysis, as the use of the proton gradient for solute transport across the mitochondrial membrane contributes significantly to the operational P/O ratio.

EXAMPLE III Phenotypic phase plane analysis [0111] This example shows how the S. cerevisiae metabolic model can be used to calculate the range of characteristic phenotypes that the organism can display as a function of variations in the activity of multiple reactions.

[0112] For this analysis, 02 and glucose uptake rates were defined as the two axes of the two-dimensional space. The optimal flux distribution was calculated using linear programming (LP) for all points in this plane by repeatedly solving the LP problem while adjusting the exchange fluxes defining the two-dimensional space. A finite number of quantitatively different metabolic pathway utilization patterns were identified in the plane, and lines were drawn to demarcate these regions. One demarcation line in the phenotypic phase plane (PhPP) was defined as the line of optimality (LO), and represents the optimal relation between the respective metabolic fluxes. The LO was identified by varying the x- axis (glucose uptake rate) and calculating the optimal y-axis (02 uptake rate), with the objective function defined as the growth flux. Further details regarding Phase-Plane Analysis are provided in Edwards et al. , Biotechnol. Bioeng. 77: 27-36 (2002) and Edwards et al., Nature Biotech. 19: 125-130 (2001)).

[0113] As illustrated in Figure 6, the S. cerevisiae PhPP contains 8 distinct metabolic phenotypes. Each region (P1-P8) exhibits unique metabolic pathway utilization that can be summarized as follows: 101141 The left-most region is the so-called"infeasible"steady state region in the PhPP, due to stoichiometric limitations.

[0115] From left to right: [0116] Pl : Growth is completely aerobic. Sufficient oxygen is available to complete the oxidative metabolism of glucose to support growth requirements. This zone represents a futile cycle. Only CO2 is formed as a metabolic by-product. The growth rate is less than the optimal growth rate in region P2. The PI upper limit represents the locus of points for which the carbon is completely oxidized to eliminate the excess electron acceptor, and thus no biomass can be generated.

[0117] P2: Oxygen is slightly limited, and all biosynthetic cofactor requirements cannot be optimally satisfied by oxidative metabolism. Acetate is formed as a metabolic by- product enabling additional high-energy phosphate bonds via substrate level phosphorylation.

With the increase Of 02 supply, acetate formation eventually decreases to zero.

[0118] P3: Acetate is increased and pyruvate is decreased with increase in oxygen uptake rate.

[0119] P4: Pyruvate starts to increase and acetate is decreased with increase in oxygen uptake rate. Ethanol production eventually decreases to zero.

[0120] P5: The fluxes towards acetate formation are increasing and ethanol production is decreasing.

[0121] P6: When the oxygen supply increases, acetate formation increases and ethanol production decreases with the carbon directed toward the production of acetate.

Besides succinate production, malate may also be produced as metabolic by-product.

[0122] P7: The oxygen supply is extremely low, ethanol production is high and succinate production is decreased. Acetate is produced at a relatively low level.

[0123] P8: This region is along the Y-axis and the oxygen supply is zero. This region represents completely anaerobic fermentation. Ethanol and glycerol are secreted as a metabolic by-product. The role of NADH-consuming glycerol formation is to maintain the cytosol redox balance under anaerobic conditions (Van Dijken and Scheffers Yeast 2 (2): 123- 7 (1986)).

[0124] Line of Optimality : Metabolically, the line of optimality (LO) represents the optimal utilization of the metabolic pathways without limitations on the availability of the substrates. On an oxygen/glucose phenotypic phase plane diagram, LO represents the optimal aerobic glucose-limited growth of S. cerevisiae metabolic network to produce biomass from unlimited oxygen supply for the complete oxidation of the substrates in the cultivation processes. The line of optimality therefore represents a completely respiratory metabolism, with no fermentation by-product secretion and the futile cycle fluxes equals zero.

[0125] Thus, this example demonstrates that Phase Plane Analysis can be used to determine the optimal fermentation pattern for S. cerevisiae, and to determine the types of organic byproducts that are accumulated under different oxygenation conditions and glucose uptake rates.

EXAMPLE IV Calculation of line of optimality and respiratory quotient [0126] This example shows how the S. cerevisiae metabolic model can be used to calculate the oxygen uptake rate (OUR), the carbon dioxide evolution rate (CER) and the respiration quotient (RQ), which is the ratio of CER over OUR.

[0127] The oxygen uptake rate (OUR) and the carbon dioxide evolution rate (CER) are direct indicators of the yeast metabolic activity during the fermentation processes. RQ is a key metabolic parameter that is independent of cell number. As illustrated in Figure 7, if the S. cerevisiae is grown along the line of optimality, LO, its growth is at optimal aerobic rate with all the carbon sources being directed to biomass formation and there are no metabolic by-products secreted except C02. The calculated RQ along the LO is a constant value of 1.06 ; the RQ in PI region is less than 1.06 ; and the RQ in the remaining regions in the yeast PhPP are greater than 1.06. The RQ has been used to determine the cell growth and metabolism and to control the glucose feeding for optimal biomass production for decades (Zeng et al. Biotechnol. Bioeng. 44: 1107-1114 (1994) ). Empirically, several researchers have proposed the values of 1.0 (Zigova, J Biotechnol 80: 55-62 (2000). Journal of Biotechnology), 1.04 (Wang et al. , Biotechnol & Bioenz 19: 69-86 (1977) ) and 1.1 (Wang et al. , Biotechnol. & Bioeng. 21: 975-995 (1979) ) as optimal RQ which should be maintained in fed-batch or continuous production of yeast's biomass so that the highest yeast biomass could be obtained (Dantigny et al. , Appl. Microbiol. Biotechnol. 36: 352-357 (1991) ). The constant RQ along the line of optimality for yeast growth by the metabolic model is thus consistent with the empirical formulation of the RQ through on-line measurements from the fermentation industry.

EXAMPLE V Computer simulations [0128] This example shows computer simulations for the change of metabolic phenotypes described by the yeast PhPP.

[0129] A piece-wise linearly increasing function was used with the oxygen supply rates varying from completely anaerobic to fully aerobic conditions (with increasing oxygen uptake rate from 0 to 20 mmol per g cell-hour). A glucose uptake rate of 5 mmol of glucose per g (dry weight-hour was arbitrarily chosen for these computations. As shown in Figure 8A, the biomass yield of the in silico S. cerevisiae strain was shown to increase from P8 to P2, and become optimal on the LO. The yield then started to slowly decline in PI (futile cycle region). At the same time, the RQ value declines in relation to the increase of oxygen consumption rate, reaching a value of 1.06 on the LO1 and then further declining to become less than 1.

[0130] Figure 8B shows the secretion rates of metabolic by-products; ethanol, succinate, pyruvate and acetate with the change of oxygen uptake rate from 0 to 20 mmol of oxygen per g (dry weight) -h. Each one of these by-products is secreted in a fundamentally different way in each region. As oxygen increases from 0 in P7, glycerol production (data not shown in this figure) decreases and ethanol production increases. Acetate and succinate are also secreted.

EXAMPLE VI Modeling of phenotypic behavior in chemostat cultures [0131] This example shows how the S. cerevisiae metabolic model can be used to predict optimal flux distributions that would optimize fermentation performance, such as specific product yield or productivity. In particular, this example shows how flux based analysis can be used to determine conditions that would minimize the glucose uptake rate of S. cerevisiae grown on glucose in a continuous culture under anaerobic and under aerobic conditions.

[0132] In a continuous culture, growth rate is equivalent to the dilution rate and is kept at a constant value. Calculations of the continuous culture of S. cerevisiae were performed by fixing the in silico growth rate to the experimentally determined dilution rate, and minimizing the glucose uptake rate. This formulation is equivalent to maximizing biomass production given a fixed glucose uptake value and was employed to simulate a continuous culture growth condition. Furthermore, a non growth dependent ATP maintenance of 1 mmol/gDW, a systemic P/O ratio of 1.5 (Verduyn et al. Antonie Van Leeuwenhoek 59 (1) : 49-63 (1991)), a polymerization cost of 23.92 mmol ATP/gDW, and a growth dependent ATP maintenance of 35.36 mmol ATP/gDW, which is simulated for a biomass yield of 0.51 gDW/h, are assumed. The sum of the latter two terms is included into the biomass equation of the genome-scale metabolic model.

[0133] Optimal growth properties of S. cerevisiae were calculated under anaerobic glucose-limited continuous culture at dilution rates varying between 0.1 and 0.4 h-l. The computed by-product secretion rates were then compared to the experimental data (Nissen et al. Microbiology 143 (1) : 203-18 (1997) ). The calculated uptake rates of glucose and the production of ethanol, glycerol, succinate, and biomass are in good agreement with the independently obtained experimental data (Figure 9). The relatively low observed acetate and pyruvate secretion rates were not predicted by the in silico model since the release of these metabolites does not improve the optimal solution of the network.

[0134] It is possible to constrain the in silico model further to secrete both, pyruvate and acetate at the experimental level and recompute an optimal solution under these additional constraints. This calculation resulted in values that are closer to the measured glucose uptake rates (Figure 9A). This procedure is an example of an iterative data-driven constraint-based modeling approach, where the successive incorporation of experimental data is used to improve the in silico model. Besides the ability to describe the overall growth yield, the model allows further insight into how the metabolism operates. From further analysis of the metabolic fluxes at anaerobic growth conditions the flux through the glucose-6-phosphate dehydrogenase was found to be 5.32% of the glucose uptake rate at dilution rate of 0.1 h-l, which is consistent with experimentally determined value (6.34%) for this flux when cells are operating with fermentative metabolism (Nissen et al., Microbiology 143 (1) : 203-218 (1997)).

[0135] Optimal growth properties of S. cerevisiae were also calculated under aerobic glucose-limited continuous culture in which the Crabtree effect plays an important role. The molecular mechanisms underlying the Crabtree effect in S. cerevisiae are not known. The regulatory features of the Crabtree effect (van Dijken et al. Antonie Van Leeuwenhoek 63 (3- 4): 343-52 (1993) ) can, however, be included in the in silico model as an experimentally determined growth rate-dependent maximum oxygen uptake rate (Overkamp et al. J. of Bacteriol 182 (10): 2823-30 (2000) )). With this additional constraint and by formulating growth in a chemostat as described above, the in silico model makes quantitative predictions about the respiratory quotient, glucose uptake, ethanol, C02, and glycerol secretion rates under aerobic glucose-limited continuous condition (Fig. 10).

EXAMPLE VII Analysis of deletion of genes involved in central metabolism in S. cerevsiae [0136] This example shows how the S. cerevisiae metabolic model can be used to determine the effect of deletions of individual reactions in the network.

[0137] Gene deletions were performed in silico by constraining the flux (es) corresponding to a specific gene to zero. The impact of single gene deletions on growth was analysed by simulating growth on a synthetic complete medium containing glucose, amino acids, as well as purines and pyrimidines.

[0138] In silico results were compared to experimental results as supplied by the Saccharomyces Genome Database (SGD) (Cherry et al. , Nucleic Acids Research 26 (1) : 73-79 (1998) ) and by the Comprehensive Yeast Genome Database (Mewes et al., Nucleic Acids Research 30 (1) : 31-34 (2002) ). In 85.6% of all considered cases (499 out of 583 cases), the in silico prediction was in qualitative agreement with experimental results. An evaluation of these results can be found in Example VIII. For central metabolism, growth was predicted under various experimental conditions and 81.5% (93 out of 114 cases) of the in silico predictions were in agreement with in vivo phenotypes.

[0139] Table 6 shows the impact of gene deletions on growth in S. cerevisiae. Growth on different media was considered, including defined complete medium with glucose as the carbon source, and minimal medium with glucose, ethanol or acetate as the carbon source.

The complete reference citations for Table 6 can be found in Table 9.

[0140] Thus, this example demonstrates that the in silico model can be used to uncover essential genes to augment or circumvent traditional genetic studies.

Table 6 Defined Medium Complete Minimal Minimal Minimal Carbon Source Glucose Glucose Acetate Ethanol in silico/in silicol in silicol in silicol References: in vivo in vivo in vivo in vivo (Minimal media) <BR> <BR> <BR> <BR> <BR> <BR> ACOI +I+-/- (Gangloff et al., 1990)<BR> <BR> <BR> <BR> CDCI9&num +/-+/ (Boles et al., 1998) CITI +/+ +/+ (Kim et al., 1986) CIT2 +/+ +/+ (Kim et al., 1986) CIT3 +/+ DAL7 +/+ +/+ +/+ +/+ (Hartig et al., 1992) NEO1 +/+ ENO255 +/- +/- FBAI* FBPI +/+ +/+ (Sedivy and Fraenkel, 1985 ; Gancedo and Delgado, 1984) FUMI +/+ GLKI +/+ GNDI""+/-+/- GND2 +/+ GPMIiI +l + GPM2 +/+ GPM3 +/+ HXKI +/+ HXK2 +/+ ICL1 +/+ +/+ (Smith et al., 1996) IDHI +/+ +/+ (Cupp and McAlister-Henn, 1992) <BR> <BR> IDH2 +/+ +/+ (Cupp and McAlister-Henn,<BR> <BR> 1992) IDP1 +/+ +/+ (Loftus et al., 1994) IDP2 +/+ +/+ (Loftus et al., 1994) IDP3 +/+ KGD1 +/+ +/+ (Repetto and Tzagoloff, 1991) KGD2 +/+ +/+ (Repetto and Tzagoloff, 1991) LPDI +/+ LSCI +/+ +/+ +/+ (Przybyla-Zawislak et al., 1998) LSC2 +/+ +/+ +/+ (Przybyla-Zawislak et al., 1998) MAEI +/+ +/+ +/+ (Boles et al., 1998) MDHI +/+ +/+ +/- (McAlister-Henn and Thompson, 1987) MDH2 +/+ +/- +/- (McAlister-Henn and Thompson, 1987) MDH3 +/+ MLSI +/+ +/+ +/+ +/+ (Hartig et al., 1992) OSMI +/+ PCKI +/+ PDCI +/+ +/+ (Flikweert et al., 1996) PDC5 +/+ +/+ (Flikweert et al., 1996) PDC6 +/+ +/+ (Flikweert et al., 1996) PFK1 +/+ +/+ (Clifton and Fraenkel, 1982) PFK2 +/+ +/+ (Clifton and Fraenkel, 1982) PG//* +/-+/- (Clifton et al., 1978) PGK1* +/- +/- PGMI +/+ +/+ (Boles et al., 1994) PGM2 +/+ +/+ (Boles et al., 1994) PYCI +/+ +/+ +/-+/- (Wills and Melham, 1985) PYC2 +/+ PYK2 +/+ +/+ +/+ (Boles et al., 1998; McAlister- Henn and Thompson, 1987) RKII -/- RPEI +/+ SOLI +/+ SOL2 +/+ <BR> <BR> SOL3 +1+<BR> <BR> <BR> SOL4 +/+ TAL1 +/+ +/+ (Schaaff-Gerstenschläger and Zimmermann, 1993) TDHI +/+ TDH2 +/+ TDH3 +/+ TKL1 +/+ +/+ (Schaff-Gerstenschläger and Zimmermann, 1993) TKL2 +/+ TPI1*.5 +/- ZWFI +/+ +/+ (Schaaff-Gerstenschläger and Zimmermann, 1993) +/-Growth/no growth # The isoenyzme Pyk2p is glucose repressed, and cannot sustain growth on glucose.

* Model predicts single deletion mutant to be (highly) growth retarded.

$ Growth of single deletion mutant is inhibited by glucose.

& Different hypotheses exist for why Pgi I p deficient mutants do not grow on glucose, e. g. the pentose phosphate pathway in S. cerevisiae is insufficient to support growth and cannot supply the EMP pathway with sufficient amounts of fructose-6-phosphate and glyceraldehydes-3-phosphate (Boles, 1997).

The isoenzymes Gpm2p and Gpm3p cannot sustain growth on glucose. They only show residual in vivo activity when they are expressed from a foreign promoter (Heinisch et al., 1998).

## Gndlp accounts for 80% of the enzyme activity. A mutant deleted in GNDI accumulates gluconate-6- phosphate, which is toxic to the cell (Schaaff-Gerstenschlager and Miosga, 1997).

$$ ENOI plays central role in gluconeogenesis whereas EN02 is used in glycolysis (Müller and Entian, 1997).

EXAMPLE VIII Large-scale gene deletion analysis in S. cerevisiae [0141] A large-scale in silico evaluation of gene deletions in S. cerevisiae was conducted using the genome-scale metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were predicted in 89.4% (496 out of 555) and lethal phenotypes are correctly predicted in 68.2% (30 out of 44) of the cases considered.

[0142] The failure modes were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: 1) incomplete media composition; 2) substitutable biomass components; 3) incomplete biochemical information; and 4) missing regulation.

This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. The genome-scale in silico model of S. cerevisiae can thus be used to systematically reconcile existing data and fill in knowledge gaps about the organism.

[0143] Growth on complete medium was simulated under aerobic condition. Since the composition of a complete medium is usually not known in detail, a synthetic complete medium containing glucose, twenty amino acids (alanine, arginine, asparagine, aspartate, cysteine, glutamine, glutamate, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine, valine) and purines (adenine and guanine) as well as pyrimidines (cytosine and thymine) was defined for modeling purposes. Furthermore, ammonia, phosphate, and sulphate were supplied. The in silico results were initially compared to experimental data from a competitive growth assay (Winzeler et al., Science 285: 901-906 (1999) ) and to available data from the MIPS and SGD databases (Mewes et al. , Nucleic Acids Research 30 (1) : 31-34 (2002); Cherry et al. , Nucleic Acids Research 26 (1) : 73-79 (1998) ). Gene deletions were simulated by constraining the flux through the corresponding reactions to zero and optimizing for growth as previously described (Edwards and Palsson, Proceedings of the National Academy of Sciences 97 (10): 5528-5533 (2000) ). For this analysis, a viable phenotype was defined as a strain that is able to meet all the defined biomass requirements and thus grow. Single gene deletion mutants that have a reduced growth rate compared to the wild type simulation are referred to as growth retarded mutants.

[0144] The analysis of experimental data was approached in three steps: The initial simulation using the synthetic medium described above, referred to as simulation 1.

False predictions of simulation 1 were subsequently examined to determine if the failure was due to incomplete information in the in silico model, such as missing reactions, the reversibility of reactions, regulatory events, and missing substrates in the synthetic complete medium. In simulation 2, any such additional information was introduced into the in silico model and growth was re-simulated for gene deletion mutants whose in silico phenotype was not in agreement with its in vivo phenotype.

A third simulation was carried out, in which dead end pathways (i. e. pathways leading to intracellular metabolites that were not further connected into the overall network), were excluded from the analysis (simulation 3).

[0145] The effect of single gene deletions on the viability of S. cerevisiae was investigated for each of the 599 single gene deletion mutants. The in silico results were categorized into four groups: 1. True negatives (correctly predicted lethal phenotype); 2. False negatives (wrongly predicted lethal phenotype); 3. True positives (correctly predicted viable phenotypes); 4. False positives (wrongly predicted viable phenotypes).

[0146] In simulation 1,509 out of 599 (85%) simulated phenotypes were in agreement with experimental data. The number of growth retarding genes in simulation 1 was counted to be 19, a surprisingly low number. Only one deletion, the deletion of TPII, had a severe impact on the growth rate. Experimentally, a deletion in TPII is lethal (Ciriacy and Breitenbach, J Bacteriol 139 (1) : 152-60 (1979)). In silico, a tpil mutant could only sustain a specific growth rate of as low as 17% of the wild type. All other growth retarding deletions sustained approximately 99% of wild type growth, with the exception of a deletion of the mitochondrial ATPase that resulted in a specific growth rate of approximately 90% of wild type.

[0147] Predictions of simulation 1 were evaluated in a detailed manner on a case-by-case basis to determine whether the false predictions could be explained by: 1. Medium composition used for the simulation; 2. The biomass composition used in the simulation; 3. Incomplete biochemical information; and 4. Effects of gene regulation.

[0148] Analysis of the false predictions from simulation 1 based on these possible failure modes resulted in model modifications that led to 526 out of 599 correctly predicted phenotypes (87.8%), i. e. simulation 2.

[0149] Simulation 3 uncovered some 220 reactions in the reconstructed network that are involved in dead end pathways. Removing these reactions and their corresponding genes from the genome-scale metabolic flux balance model, simulation 3 resulted in 473 out of 530 (89.6%) correctly predicted phenotypes of which 91.4% are true positive and 69.8% are true negative predictions.

[0150] Table 7 provides a summary of the large-scale evaluation of the effect of in silico single gene deletions in S. cerevisiae on viability.

Table 7 Genes Simulation 1 2 involved in 3 dead end pathways Number of deletion 599 599 530 Predicted Total 509 526 475 True positive 481 496 51 445 True negative 28 30 0 30 False positive 63 59 17 42 False negative 27 14 1 13 Overall Prediction 85.0% 87.8% 89.6% Positive Prediction 88.4% 89.4% 91.4% Negative Prediction 50. 9% 68.2% 69.8% [0151] A comprehensive list of all the genes used in the in silico deletion studies and results of the analysis are provided in Table 8. Table 8 is organized according to the categories true negative, false negative, true positive and false positive predictions. Genes highlighted in grey boxes, such as, corresponded initially to false predictions (simulation 1); however, evaluation of the false prediction and simulation 2 identified these cases as true predictions. ORFs or genes that are in an open box, such as torr were excluded in simulation 3, as the corresponding reactions catalysed steps in dead end pathways.

Table 8 False Positive ACS2 URl BET2 CDCI9 CDC21 CDC8 CYRI ED81 DFRI IMI DUTl YSI EN02 ERGIO ERGI3 FADI FMNI FOL1 FOL2 FOL3 GFAI GPMI EMl EMI EMI3 EMI E EM3 EM HIPI TSI ILV3 ILVS SI LCBI LCB2 MSS4 NAT2 NCPI MTI PCMI PET9 GSl IKI PMAI PR03 QNSI QRII RER2 RIB5 SEC55| STT4 TH180 TOR2 TPI TSC10 UGP1 URA6 YDR341C YGL245W False Negative ADE3 ADKI CHOI CH02 DPPI ERG3 ERG4 ERG5 ERG6 INMI MET6 OPI3 PPT2 YNKI True Negative ACC1 m CDS1 DPM1 ERG1 ERG7 ERG8 ERG9 ERGI I ERG12 ERG20 ERG25 ERG26 ERG27 FBAI G GUKI IDI ! IPPI MVDI PGII PGKI PISI PMI40PSAl RKII SAHI SEC53 TRRI YDR537W True Positive AACI AAC3AAHI AATI AAT2ABZI ACOI ACSI ADE1ADE12ADE16ADE1ZADE2ADE4ADE5ADE6 ADE7 ADE8 ADHI ADH2 ADH3 ADH4 ADH5 ADK2 A GPI GP2 GP3 ALD2 ALD3 ALD4 ALD5 ALD6 ALPI ASP ATHI ATPI BAP2BAP3BATI BAT2BGL2BEE3CANI CARI CAR2 CA T2 CDA I CDA 2 CDDI CEMI CHAI CHSI CHS2 CHS3 CITI CIT2 CIT3 CKII COQI COQ2 COQ3 CO CO COXI COXI 0 CPA 2 CRCI EJ CSG2 CTA I CTPI CTTI m CYS3 CYS4 DAKI DAK2 DALI DAL2 DAL3DAL4DAL5|DAL7DCDI DEGI DICI DIP5DLDI DPHS|DPLI DURI DUR3ECMI7E ECM40 ECTI S ENOI E m ERRI ERR2 EXGI EXG2 FAAI FAA2 FAA3 FAA4 FABI m FBPI FBP26 FCYI FCY2 FKSI FKS3 FLXI m ES3 FRDS FUII FUMI FUN63 FURI FUR4 GADI GALS GAL10 GAL2 GAL7 GAPI GCVI GCV2 GDHI GDH2 GDH3 GLC3 GLKI GLOI GLO GLO GLRI GLTI GLYI GNAI GNDI GND2 GNPI GPDI GPD2 GPHI GPM2 GPM3 GPXI GPX2 GSC2 GSHI GSH2 GSYI GSY2 GUA I GUTl GUT2 EMI HISI HIS2 HIS3 HIS4 HIS5 HIS6 HIS7 HMGI HMG2 MTl HNMI HOM2 HOM3 HOM6 HOR2 HPTI HXKI HXK2 HXTI HXT10 HXTII HXTI3 HXT74 HXTlS HXTl6 HXTI7HXT2HXT3HXT4HXT5HXT6HXT7HXT8HXT9HYRI ICLI ICL2IDHI IDPI IDP21DP31LVI r IL V2 W BTRI ITR2 JENI KCDI KRE2 KTR I KTR2 KTR3 KTR4 KTR6 LCB3 LCB4 LCB5 m LEU2 LEU4 PD1 PPl LSCI LSC2 LYPI LYSI GYSI 2 LYS2 LYS20 LYS21 LYS4 LYS9 MAEI MAK3 MAL12 MAL31 MAL32MDHI MDH2MDH3MELI MEPI MEP2MEP3 EMETI0METI2METI3METI4 METI 6 METI 7 MET2 MET22 MET3 MET7 MHTI MIRI MISI MLSI WMPII 23 E MSRI WSW/| MTDI MUPI MUP3 NATI NDHI NDH2 NDII WHAI| E3 NPTI NTAI NTHI NTH2 OACI ODCI ODC2 ORTI OSMI PADI PCKI E3PDAI PDCI PDC5PDC6PDEI PDE2PDX3PFKI PFK2PFK26PFK27 PCMI PCM2 PHA2 PHO8 PHOI I E m PMA2 PMPI PMP2 PMTI PMT2 PMT3 PMT4 PMT5 PMT6 Fi3 PNPI POSS OTl PPA2 PRM4 PRM5 PRM6 PROI PR02 PRSI PRS2 PRS3 PRS4 PRSS SDl PTR2 PUR5 PUSI PUS2 PUS4 PUTI PUT2 PUT4 PYCI PYC2 PYK2 QPTI RAMI RBKI RHR2 RIBI RIB4 RIB 7 RMA I RNR I RNR3 RPEI SAMI SAM2 SAM3 SAM4 m SDH3 SER I SER2 SER3 SER33 SFA I SFCl SHMI SHM2 SLCI SOLI SOL2 SOL3 SOL4 ORI SPEI SPE2 SPE3 SPE4 SPRI SRTI STLI SUC2 SULI SUL2 SURI SUR2 TALI TATl TAT2 TDHI TDH2 TDH3 THI20 THI21 THI22 THI6 THI7 THM2 THM3 THRI THR4 TKLI TKL2 TORI TPSI X TPS3 EJ TRPI TRP2 TRP3 TRP4 TRP5 m TSLI TYRI I/G7 GSJEiL4L47L4L470L//7 M ! 7 (/7K7 f'/Ytyr7 UGLI I UGA4 URA5 URA 7 URA8 URA 10 URHI URKI UTRI VAPI VPS34 XPTI YATI YSR3 YURI ZWFI YBL098 YBR006W YBR284W YDLIOOC YDRlIIC YEL041 W YER053C YFL030W YFR055W YGR012W YGR043C YGR125W YGR287C ; ' YILI67W YJL070C YJL200C YJL216C YJL218W YJR078W YLR089 YLR231 C YLR328W YML082 YMR293C [0152] The following text describes the analysis of the initially false predictions of simulation 1 that were performed, leading to simulation 2 results.

Influence of media composition on simulation results: [0153] A rather simple synthetic complete medium composition was chosen for simulation 1. The in silico medium contained only glucose, amino acids and nucleotides as the main components. However, complete media often used for experimental purposes, e. g. the YPD medium containing yeast extract and peptone, include many other components, which are usually unknown.

[0154] False negative predictions: The phenotype of the following deletion mutants: <BR> <BR> <BR> eomla, yil145c#, erg2 #, erg24 #, fas1 #, ural #, ura2 #, ura3 # and ura4 # were falsely predicted to be lethal in simulation 1. In simulation 2, an additional supplement of specific substrate could rescue a viable phenotype in silico and as the supplemented substrate may be assumed to be part of a complex medium, the predictions were counted as true positive predictions in simulation 2. For example, both Ecml and Yill45c are involved in pantothenate synthesis. Ecml catalyses the formation of dehydropantoate from 2- oxovalerate, whereas Yi1145c catalyses the final step in pantothenate synthesis from- alanine and panthoate. In vivo, ecmld, and yill45c d mutants require pantothenate for growth (White et al. , J Biol Chem 276 (14): 10794-10800 (2001)). By supplying pantothenate to the synthetic complete medium in silico, the model predicted a viable phenotype and the growth rate was similar to in silico wild type S. cerevisiae.

[0155] Similarly other false predictions could be traced to medium composition: * Mutants deleted in ERG2 or ERG24 are auxotroph for ergosterol (Silve et al., Mol Cell Biol 16 (6): 2719-2727 (1996); Bourot and Karst, Gene 165 (1) : 97-102 (1995)).

Simulating growth on a synthetic complete medium supplemented with ergosterol allowed the model to accurately predict viable phenotypes.

A deletion of FAS1 (fatty acid synthase) is lethal unless appropriate amounts of fatty acids are provided, and by addition of fatty acids to the medium, a viable phenotype was predicted.

Strains deleted in URAI, URA2, URA3, or URA4 are auxotroph for uracil (Lacroute, J Bacteriol 95 (3): 824-832 (1968) ), and by supplying uracil in the medium the model predicted growth.

[0156] The above cases were initially false negative predictions, and simulation 2 demonstrated that these cases were predicted as true positive by adjusting the medium composition.

[0157] False positive predictions: Simulation 1 also contained false positive predictions, which may be considered as true negatives or as true positives. Contrary to experimental results from a competitive growth assay (Winzeler et al. , Science 285: 901-906 (1999)), mutants deleted in ADEl3 are viable in vivo on a rich medium supplemented with low concentrations of adenine, but grow poorly (Guetsova et al. , Genetics 147 (2): 383-397 (1997) ). Adenine was supplied in the in silico synthetic complete medium. By not supplying adenine, a lethal mutant was predicted. Therefore, this case was considered as a true negative prediction.

[0158] A similar case was the deletion of GLN1, which codes a glutamine synthase, the only pathway to produce glutamine from ammonia. Therefore, glnld mutants are glutamine auxotroph (Mitchell, Genetics 111 (2) : 243-58 (1985) ). In a complex medium, glutamine is likely to be deaminated to glutamate, particularly during autoclaving. Complex media are therefore likely to contain only trace amounts of glutamine, and glnld mutants are therefore not viable. However, in silico, glutamine was supplied in the complete synthetic medium and growth was predicted. By not supplying glutamine to the synthetic complete medium, the model predicted a lethal phenotype resulting in a true negative prediction.

[0159] Ilv3 and Ilv5 are both involved in branched amino acid metabolism. One may expect that a deletion of IL V3 or IL V5 could be rescued with the supply of the corresponding amino acids. For this, the model predicted growth. However, contradictory experimental data exists. In a competitive growth assay lethal phenotypes were reported. However, earlier experiments showed that ilv3A and ilv5A mutants could sustain growth when isoleucine and valine were supplemented to the medium, as for the complete synthetic medium. Hence, these two cases were considered to be true positive predictions.

Influence of the definition of the biomass equation [0160] The genome-scale metabolic model contains the growth requirements in the form of biomass composition. Growth is defined as a drain of building blocks, such as amino acids, lipids, nucleotides, carbohydrates, etc. , to form biomass. The number of biomass components is 44 (see Table 1). These building blocks are essential for the formation of cellular components and they have been used as a fixed requirement for growth in the in silico simulations. Thus, each biomass component had to be produced by the metabolic network otherwise the organism could not grow in silico. In vivo, one often finds deletion mutants that are not able to produce the original biomass precursor or building block; however, other metabolites can replace these initial precursors or building blocks. Hence, for a number of strains a wrong phenotype was predicted in silico for this reason.

101611 Phosphatidylcholine is synthesized by three methylation steps from phosphatidylethanolamine (Dickinson and Schweizer, The metabolism and molecular physiology of Saccharomyces cerevisiae Taylor & Francis, London; Philadelphia (1999)).

The first step in the synthesis of phosphatidylcholine from phosphatidylethanolamine is catalyzed by a methyltransferase encoded by CH02 and the latter two steps are catalyzed by phospholipid methyltransferase encoded by OPI3. Strains deleted in CH02 or OPI3 are viable (Summers et al., Genetics 120 (4): 909-922 (1988); Daum et al. , Yeast 14 (16): 1471- 1510 (1998) ) ; however, either null mutant accumulates mono-and dimethylated phosphatidylethanolamine under standard conditions and display greatly reduced levels of phosphatidylcholine (Daum et al., Yeast 15 (7): 601-614 (1999) ). Hence, phosphatidylethanolamine can replace phosphatidylcholine as a biomass component. In silico, phosphatidylcholine is required for the formation of biomass. One may further speculate on whether an alternative pathway for the synthesis of phosphatidylcholine is missing in the model, since Daum et al., supra (1999) detected small amounts of phosphatidylcholine in cho20 mutants. An alternative pathway, however, was not included in the in silico model.

[0162] Deletions in the ergosterol biosynthetic pathways of ERG3, ERG4, ERG5 or ERG6 lead in vivo to viable phenotypes. The former two strains accumulate ergosta-8,22, 24 (28)- trien-3-beta-ol (Bard et al., Lipids 12 (8): 645-654 (1977); Zweytick et al. , FEBS Lett 470 (1) : 83-87 (2000) ), whereas the latter two accumulate ergosta-5,8-dien-3beta-ol (Hata et al. , J Biochem (Tokyo) 94 (2): 501-510 (1983) ), or zymosterol and smaller amounts of cholesta- 5,7, 24-trien-3-beta-ol and cholesta-5,7, 22,24-trien-3-beta-ol (Bard et al. , supra (1977); Parks et al. , Crit Rev Biochem Mol Biol 34 (6): 399-404 (1999) ), respectively, components that were not included in the biomass equations.

101631 The deletion of the following three genes led to false positive predictions : RER2, SEC59 and QIR1. The former two are involved in glycoprotein synthesis and the latter is involved in chitin metabolism. Both chitin and glycoprotein are biomass components.

However, for simplification, neither of the compounds was considered in the biomass equation. Inclusion of these compounds into the biomass equation may improve the prediction results.

Incomplete biochemical information [0164] For a number of gene deletion mutants (inmlA, met6A, ynkl0, pho84A. psd2A, tps2A), simulation 1 produced false predictions that could not be explained by any of the two reasons discussed above nor by missing gene regulation (see below). Further investigation of the metabolic network including an extended investigation of biochemical data from the published literature showed that some information was missing initially in the in silico model or information was simply not available.

[0165] Inml catalyses the ultimate step in inositol biosynthesis from inositol 1-phosphate to inositol (Murray and Greenberg, Mol Microbiol 36 (3): 651-661 (2000) ). Upon deleting INM1, the model predicted a lethal phenotype in contrary to the experimentally observed viable phenotype. An isoenzyme encoded by IMP2 was initially not included in the model, which may take over the function of INMI and this addition would have led to a correct prediction. However, an inmlaimp2zl in vivo double deletion mutant is not inositol auxotroph (Lopez et al. , Mol Microbiol 31 (4): 1255-1264 (1999) ). Hence, it appears that alternative routes for the production of inositol probably exist. Due to the lack of comprehensive biochemical knowledge, effects on inositol biosynthesis and the viability of strains deleted in inositol biosynthetic genes could not be explained.

[0166] Met6 mutants are methionine auxotroph (Thomas and Surdin-Kerjan, Microbiol Mol Biol Rev 61 (4): 503-532 (1997) ), and growth may be sustained by the supply of methionine or S-adenosyl-L-methionine. In silico growth was supported neither by the addition of methionine nor by the addition of S-adenosyl-L-methionine. Investigation of the metabolic network showed that deleting MET6 corresponds to deleting the only possibility for using 5-methyltetrahydrofolate. Hence, the model appears to be missing certain information. A possibility may be that the carbon transfer is carried out using 5- methyltetrahydropteroyltri-L-glutamate instead of 5-methyltetrahydrofolate. A complete pathway for such a by-pass was not included in the genome-scale model.

[0167] The function of Ynklp is the synthesis of nucleoside triphosphates from nucleoside diphosphates. YNKIA mutants have a 10-fold reduced Ynklp activity (Fukuchi et al., Genes 129 (1) : 141-146 (1993) ), though this implies that there may either be an alternative route for the production of nucleoside triphosphates or a second nucleoside diphosphate kinase, even though there is no ORF in the genome with properties that indicates that there is a second nucleoside diphosphate kinase. An alternative route for the production of nucleoside triphosphate is currently unknown (Dickinson et al. , supra (1999) ), and was therefore not included in the model, hence a false negative prediction.

[0168] PH084 codes for a high affinity phosphate transporter that was the only phosphate transporter included in the model. However, at least two other phosphate transporters exist, a second high affinity and Na+ dependent transporter Pho89 and a low affinity transporter (Persson et al. , Biochim Biophys Acta 1422 (3): 255-72 (1999) ). Due to exclusion of these transporters a lethal pho842 mutant was predicted. Including PH089 and a third phosphate transporter, the model predicted a viable deletion mutant.

[0169] In a null mutant of PSD2, phosphatidylethanolamine synthesis from phosphatidylserine is at the location of Psdl (Trotter et al. , J Biol Chem 273 (21): 13189- 13196 (1998) ), which is located in the mitochondria. It has been postulated that phosphatidylserine can be transported into the mitochondria and phosphatidylethanolamine can be transported out of the mitochondria. However, transport of phosphatidylethanolamine and phosphatidylserine over the mitochondrial membrane was initially not included in the model. Addition of these transporters to the genome-scale flux balance model allowed in silico growth of a PSD2 deleted mutant.

[0170] Strains deleted in TPS2 have been shown to be viable when grown on glucose (Bell et al. , J Biol Chem 273 (50): 33311-33319 (1998) ). The reaction carried out by Tps2p was modeled as essential and as the final step in trehalose synthesis from trehalose 6- phosphate. However, the in vivo viable phenotype shows that other enzymes can take over the hydrolysis of trehalose 6-phosphate to trehalose from Tps2p (Bell et al., supra (1998) ).

The corresponding gene (s) are currently unknown. Inclusion of a second reaction catalyzing the final step of trehalose formation allowed for the simulation of a viable phenotype.

[0171] Strains deleted in ADE3 (C 1-tetrahydrofolate synthase) and ADKI (Adenylate kinase) could not be readily explained. It is possible that alternative pathways or isoenzyme- coding genes for both functions exist among the many orphan genes still present in the S. cerevisiae.

[0172] The reconstruction process led to some incompletely modeled parts of metabolism.

Hence, a number of false positive predictions may be the result of gaps (missing reactions) within pathways or between pathways, which prevent the reactions to completely connect to the overall pathway structure of the reconstructed model. Examples include: Sphingolipid metabolism. It has not yet been fully elucidated and therefore was not included completely into the model nor were sphingolipids considered as building blocks in the biomass equation.

Formation of tRNA. During the reconstruction process some genes were included responsible for the synthesis of tRNA (DED81, HTSI, KRSI, YDR41C, YGL245W).

However, pathways of tRNA synthesis were not fully included.

# Heme synthesis was considered in the reconstructed model (HEM1, HEM12, HEM13, HEM15, HEM2, HEM3, HEM4). However no reaction was included that metabolized heme in the model.

Hence, the incomplete structure of metabolic network may be a reason for <BR> <BR> <BR> <BR> <BR> false prediction of the phenotype of aurld, lcbld, Icb2d, tsclOd, ded8ld, htsld,<BR> <BR> <BR> <BR> <BR> <BR> <BR> <BR> krsld, ydr4lcd, ygl245wd, hemld, heml2d, heml3d, hemlSd, hem2d, hem3J, and hem4 deletion mutants. Reaction reversibility. The CHO1 gene encodes a phosphatidylserine synthase, an integral membrane protein that catalyses a central step in cellular phospholipid biosynthesis. In vivo, a deletion in CHO1 is viable (Winzeler et al., Science 285: 901-906 (1999) ). However, mutants are auxotrophic for choline or ethanolamine on media containing glucose as the carbon source (Birner et al. , Mol Biol Cell 12 (4): 997-1007 (2001)).

* Nevertheless, the model did not predict growth when choline and/or ethanolamine were supplied. Further investigation of the genome-scale model showed that this might be due to defining reactions leading from phosphatidylserine to phosphatidylcholine via phosphatidylethanolamine exclusively irreversible. By allowing these reactions to be reversible, either supply of choline and ethanolamine could sustain growth in silico.

Gene Regulation [0173] Whereas many false negative predictions could be explained by either simulation of growth using the incorrect in silico synthetic complete medium or by initially missing information in the model, many false positives may be explained by in vivo catabolite expression, product inhibition effects or by repressed isoenzymes, as kinetic and other regulatory constraints were not included in the genome-scale metabolic model.

[0174] A total of 17 false positive predictions could be related to regulatory events. For a deletion of CDCl9, ACS2 or EN02 one may usually expect that the corresponding isoenzymes may take over the function of the deleted genes. However, the corresponding genes, either PYK2, ACS1 or ENO1, respectively, are subject to catabolite repression (Boles et al. , J Bacteriol 179 (9): 2987-2993 (1997); van den Berg and Steensma, Eur J Biochem 231 (3): 704-713 (1995); Zimmerman et al. , Yeast sugar metabolism: biochemistry, genetics, biotechnology, and applications Technomic Pub. , Lancaster, PA (1997) ). A deletion of GPM1 should be replaced by either of the two other isoenzymes, Gpm2 and Gpm3; however for the two latter corresponding gene products usually no activity is found (Heinisch et al., Yeast 14 (3): 203-13 (1998)).

[0175] Falsely predicted growth phenotypes can often be explained when the corresponding deleted metabolic genes are involved in several other cell functions, such as cell cycle, cell fate, communication, cell wall integrity, etc. The following genes whose deletions yielded false positive predictions were found to have functions other than just metabolic function: ACS2, BET2, CDCl9, CDC8, CYR1, DIM1, EN02, FAD1, GFA1, GPMI, HIP1, MSS4, PET9, Pill, PMAI, STT4, TOR2. Indeed, a statistical analysis of the MIPS functional catalogue (http://mips. gsf. de/proj/yeast/) showed that in general it was more likely to have a false prediction when the genes that had multiple functions were involved in cellular communication, cell cycling and DNA processing or control of cellular organization.

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[0176] Throughout this application various publications have been referenced. The disclosures of these publications in their entireties are hereby incorporated by reference in this application in order to more fully describe the state of the art to which this invention pertains.

[0177] Although the invention has been described with reference to the examples provided above, it should be understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is only limited by the claims.