METHODS FOR IDENTIFYING DRUG TARGETS BASED ON GENOMIC SEQUENCE DATA

Abstract: This invention provides a computational approach to identifying potential antibacterial drug targets based on a genome sequence and its annotation. Starting from a fully sequenced genome, open reading frame assignments are made which determine the metabolic genotype for the organism. The metabolic genotype, and more specifically its stoichiometric matrix, are analyzed using flux balance analysis to assess the effects of genetic deletions on the fitness of the organism and its ability to produce essential biomolecules required for growth.

Description: METHODS FOR IDENTIFYING DRUG TARGETS BASED ON GENOMIC SEQUENCE DATA Background of the Invention Field of the Invention This invention relates to methods for identifying drug targets based on geno ic sequence data. More specifically, this invention relates to systems and methods for determining suitable molecular targets for the directed development of antimicrobial agents. Description of the Related Art Infectious disease is on a rapid rise and threatens to regain its status as a major health problem. Prior to the discovery of antibiotics in the 1930s, infectious disease was a major cause of death. Further discoveries, development, and mass production of antibiotics throughout the 1940s and 1950s dramatically reduced deaths from microbial infections to a level where they effectively no longer represented a major threat in developed countries. Over the years antibiotics have been liberally prescribed and the strong selection pressure that this represents has led to the emergence of antibiotic resistant strains of many serious human pathogens. In some cases selected antibiotics, such as vancomycin, literally represent the last line of defense against certain pathogenic bacteria such as Staphylococcus. The possibility for staphylococci to acquire vancomycin resistance through exchange of genetic material with enterococci, which are commonly resistant to vancomycin, is a serious issue of concern to health care specialists. The pharmaceutical industry continues its search for new antimicrobial compounds, which is a lengthy and tedious, but very important process. The rate of development and introduction of new antibiotics appears to no longer be able to keep up with the evolution of new antibiotic resistant organisms. The rapid emergence of antibiotic resistant organisms threatens to lead to a serious widespread health care concern. The basis of antimicrobial chemotherapy is to selectively kill the microbe with minimal, and ideally no, harm to normal human cells and tissues. Therefore, ideal targets for antibacterial action are biochemical processes that are unique to bacteria, or those that are sufficiently different from the corresponding mammalian processes to allow acceptable discrimination between the two. For effective antibiotic action it is clear that a vital target must exist in the bacterial cell and that the antibiotic be delivered to the target in an active form. Therefore resistance to an antibiotic can arise from: (i) chemical destruction or inactivation of the antibiotic; (ii) alteration of the target site to reduce or eliminate effective antibiotic binding; (iii) blocking antibiotic entry into the cell, or rapid removal from the cell after entry; and (iv) replacing the metabolic step inhibited by the antibiotic. Thus, it is time to fundamentally re-examine the philosophy of microbial killing strategies and develop new paradigms. One such paradigm is a holistic view of cellular metabolism. The identification of "sensitive" metabolic steps in attaining the necessary metabolic flux distributions to support growth and survival that can be attacked to weaken or destroy a microbe, need not be localized to a single biochemical reaction or cellular process. Rather, different cellular targets that need not be intimately related in the metabolic topology could be chosen based on the concerted effect the loss of each of these functions would have on metabolism. A similar strategy with viral infections has recently proved successful. It has been shown that "cocktails" of different drugs that target different biochemical processes provide enhanced success in fighting against HIV infection. Such a paradigm shift is possible only if the necessary biological information as well as appropriate methods of rational analysis are available. Recent advances in the field of genomics and bioinformatics, in addition to mathematical modeling, offer the possibility to realize this approach. At present, the field of microbial genetics is entering a new era where the genomes of several microorganisms are being completely sequenced. It is expected that in a decade, or so, the nucleotide sequences of the genomes of all the major human pathogens will be completely determined. The sequencing of the genomes of pathogens such as Haemophilus influenzae has allowed researchers to compare the homology of proteins encoded by the open reading frames (ORFs) with those of Escherichia coll, resulting in valuable insight into the H. influenzae metabolic features. Similar analyses, such as those performed with H. influenzae, will provide details of metabolism spanning the hierarchy of metabolic regulation from bacterial genomes to phenotypes. These developments provide exciting new opportunities to carry out conceptual experiments in silico to analyze different aspects of microbial metabolism and its regulation. Further, the synthesis of whole-cell models is made possible. Such models can account for each and every single metabolic reaction and thus enable the analysis of their role in overall cell function. To implement such analysis, however, a mathematical modeling and simulation framework is needed which can incorporate the extensive metabolic detail but still retain computational tractability. Fortunately, rigorous and tractable mathematical methods have been developed for the required systems analysis of metabolism. A mathematical approach that is well suited to account for genomic detail and avoid reliance on kinetic complexity has been developed based on well-known stoichiometry of metabolic reactions. This approach is based on metabolic flux balancing in a metabolic steady state. The history of flux balance models for metabolic analyses is relatively short. It has been applied to metabolic networks, and the study of adipocyte metabolism. Acetate secretion from £ coll under ATP maximization conditions and ethanol secretion by yeast have also been investigated using this approach. The complete sequencing of a bacterial genome and ORF assignment provides the information needed to determine the relevant metabolic reactions that constitute metabolism in a particular organism. Thus a flux-balance model can be formulated and several metabolic analyses can be performed to extract metabolic characteristics for a particular organism. The flux balance approach can be easily applied to systematically simulate the effect of single, as well as multiple, gene deletions. This analysis will provide a list of sensitive enzymes that could be potential antimicrobial targets. The need to consider a new paradigm for dealing with the emerging problem of antibiotic resistant pathogens is a problem of vital importance. The route towards the design of new antimicrobial agents must proceed along directions that are different from those of the past. The rapid growth in bioinformatics has provided a wealth of biochemical and genetic information that can be used to synthesize complete representations of cellular metabolism. These models can be analyzed with relative computational ease through flux-balance models and visual computing techniques. The ability to analyze the global metabolic network and understand the robustness and sensitivity of its regulation under various growth conditions offers promise in developing novel methods of antimicrobial chemotherapy. In one example, Pramanik et al. described a stoichiometric model of £ coli metabolism using flux-balance modeling techniques [Stoichiometric Model of Escherichia coli Metabolism: Incorporation of Growth-Rate Dependent Biomass Composition and Mechanistic Energy Requirements, Biotechnology and Bioenqineerinq. Vol. 56, No. 4, November 20, 1997). However, the analytical methods described by Pramanik, et al. can only be used for situations in which biochemical knowledge exists for the reactions occurring within an organism. Pramanik, et al. produced a metabolic model of metabolism for £ coli based on biochemical information rather than genomic data since the metabolic genes and related reactions for £ col/ ' haά already been well studied and characterized. Thus, this method is inapplicable to determining a metabolic model for organisms for which little or no biochemical information on metabolic enzymes and genes is known. It can be envisioned that in the future the only information we may have regarding an emerging pathogen is its genomic sequence. What is needed in the art is a system and method for determining and analyzing the entire metabolic network of organisms whose metabolic reactions have not yet been determined from biochemical assays. The present invention provides such a system. Summary of the Invention This invention relates to constructing metabolic genotypes and genome specific stoichiometric matrices from genome annotation data. The functions of the metabolic genes in the target organism are determined by homology searches against databases of genes from similar organisms. Once a potential function is assigned to each metabolic gene of the target organism, the resulting data is analyzed. In one embodiment, each gene is subjected to a flux- balance analysis to assess the effects of genetic deletions on the ability of the target organism to produce essential biomolecuies necessary for its growth. Thus, the invention provides a high-throughput computational method to screen for genetic deletions which adversely affect the growth capabilities of fully sequenced organisms. Embodiments of this invention also provide a computational, as opposed to an experimental, method for the rapid screening of genes and their gene products as potential drug targets to inhibit an organism's growth. This invention utilizes the genome sequence, the annotation data, and the biomass requirements of an organism to construct genomicaily complete metabolic genotypes and genome-specific stoichiometric matrices. These stoichiometric matrices are analyzed using a flux-balance analysis. This invention describes how to assess the affects of genetic deletions on the fitness and productive capabilities of an organism under given environmental and genetic conditions. Construction of a genome-specific stoichiometric matrix from genomic annotation data is illustrated along with applying flux-balance analysis to study the properties of the stoichiometric matrix, and hence the metabolic genotype of the organism. By limiting the constraints on various fluxes and altering the environmental inputs to the metabolic network, genetic deletions may be analyzed for their affects on growth. This invention is embodied in a software application that can be used to create the stoichiometric matrix for a fully sequenced and annotated genome. Additionally, the software application can be used to further analyze and manipulate the network so as to predict the ability of an organism to produce biomolecuies necessary for growth, thus, essentially simulating a genetic deletion. Brief Description of the Drawings Figure 1 is a flow diagram illustrating one procedure for creating metabolic genotypes from genomic sequence data for any organism. Figure 2 is a flow diagram illustrating one procedure for producing in silico microbial strains from the metabolic genotypes created by the method of Figure 1, along with additional biochemical and microbiological data. Figure 3 is a graph illustrating a prediction of genome scale shifts in transcription. The graph shows the different phases of the metabolic response to varying oxygen availability, starting from completely aerobic to completely anaerobic in £ coli. The predicted changes in expression pattern between phases II and V are indicated. Detailed Description of the Preferred Embodiment This invention relates to systems and methods for utilizing genome annotation data to construct a stoichiometric matrix representing most of all of the metabolic reactions that occur within an organism. Using these systems and methods, the properties of this matrix can be studied under conditions simulating genetic deletions in order to predict the affect of a particular gene on the fitness of the organism. Moreover, genes that are vital to the growth of an organism can be found by selectively removing various genes from the stoichiometric matrix and thereafter analyzing whether an organism with this genetic makeup could survive. Analysis of these lethal genetic mutations is useful for identifying potential genetic targets for anti microbial drugs. It should be noted that the systems and methods described herein can be implemented on any conventional host computer system, such as those based on Intel® microprocessors and running Microsoft Windows operating systems. Other systems, such as those using the UNIX or LINUX operating system and based on IBM®, DEC® or Motorola® 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. Software to implement the system 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 the host computer system. Such a memory can be a hard disk, Random Access Memory, Read Only Memory and Flash Memory. Other types of memories are also contemplated to function within the scope of the invention. A process 10 for producing metabolic genotypes from an organism is shown in Figure 1. Beginning at a start state 12, the process 10 then moves to a state 14 to obtain the genomic DNA sequence of an organism. The nucleotide sequence of the genomic DNA can be rapidly determined for an organism with a genome size on the order of a few million base pairs. One method for obtaining the nucleotide sequences in a genome is through commercial gene databases. Many gene sequences are available on-line through a number of sites (see, for example, www.tinr.org) and can easily be downloaded from the Internet. Currently, there are 16 microbial genomes that have been fully sequenced and are publicly available, with countless others held in proprietary databases. It is expected that a number of other organisms, including pathogenic organisms will be found in nature for which little experimental information, except for its genome sequence, will be available. Once the nucleotide sequence of the entire genomic DNA in the target organism has been obtained at state 14, the coding regions, also known as open reading frames, are determined at a state 16. Using existing computer algorithms, the location of open reading frames that encode genes from within the genome can be determined. For example, to identify the proper location, strand, and reading frame of an open reading frame one can perform a gene search by signal (promoters, ribosomal binding sites, etc.) or by content (positional base frequencies, codon preference). Computer programs for determining open reading frames are available, for example, by the University of Wisconsin Genetics Computer Group and the National Center for Biotechnology Information. After the location of the open reading frames have been determined at state 16, the process 10 moves to state 18 to assign a function to the protein encoded by the open reading frame. The discovery that an open reading frame or gene has sequence homology to a gene coding for a protein of known function, or family of proteins of known function, can provide the first clues about the gene and it's related protein's function. After the locations of the open reading frames have been determined in the genomic DNA from the target organism, well-established algorithms (i.e. the Basic Local Alignment Search Tool (BLAST) and the FAST family of programs can be used to determine the extent of similarity between a given sequence and gene/protein sequences deposited in worldwide genetic databases. If a coding region from a gene in the target organism is homologous to a gene within one of the sequence databases, the open reading frame is assigned a function similar to the homologously matched gene. Thus, the functions of nearly the entire gene complement or genotype of an organism can be determined so long as homologous genes have already been discovered. All of the genes involved in metabolic reactions and functions in a cell comprise only a subset of the genotype. This subset of genes is referred to as the metabolic genotype of a particular organism. Thus, the metabolic genotype of an organism includes most or all of the genes involved in the organism's metabolism. The gene products produced from the set of metabolic genes in the metabolic genotype carry out all or most of the enzymatic reactions and transport reactions known to occur within the target organism as determined from the genomic sequence. To begin the selection of this subset of genes, one can simply search through the list of functional gene assignments from state 18 to find genes involved in cellular metabolism. This would include genes involved in central metabolism, amino acid metabolism, nucleotide metabolism, fatty acid and lipid metabolism, carbohydrate assimilation, vitamin and cofactor biosynthesis, energy and redox generation, etc. This subset is generated at a state 20. The process 10 of determining metabolic genotype of the target organism from genomic data then terminates at an end stage 22. Referring now to Figure 2, the process 50 of producing a computer model of an organism. This process is also known as producing in silico microbial strains. The process 50 begins at a start state 52 (same as end state 22 of process 10) and then moves to a state 54 wherein biochemical information is gathered for the reactions performed by each metabolic gene product for each of the genes in the metabolic genotype determined from process 10. For each gene in the metabolic genotype, the substrates and products, as well as the stoichio etry of any and all reactions performed by the gene product of each gene can be determined by reference to the biochemical literature. This includes information regarding the irreverisble or reversible nature of the reactions. The stoichiometry of each reaction provides the molecular ratios in which reactants are converted into products. Potentially, there may still remain a few reactions in cellular metabolism which are known to occur from in vitro assays and experimental data. These would include well characterized reactions for which a gene or protein has yet to be identified, or was unidentified from the genome sequencing and functional assignment of state 14 and 18. This would also include the transport of metabolites into or out of the cell by uncharacterized genes related to transport. Thus one reason for the missing gene information may be due to a lack of characterization of the actual gene that performs a known biochemical conversion. Therefore upon careful review of existing biochemical literature and available experimental data, additional metabolic reactions can be added to the list of metabolic reactions determined from the metabolic genotype from state 54 at a state 56. This would include information regarding the substrates, products, reversibilty/irreversibility, and stoichiometry of the reactions. All of the information obtained at states 54 and 56 regarding reactions and their stoichiometry can be represented in a matrix format typically referred to as a stoichiometric matrix. Each column in the matrix corresponds to a given reaction or flux, and each row corresponds to the different metabolites involved in the given reaction/flux. Reversible reactions may either be represented as one reaction that operates in both the forward and reverse direction or be decomposed into one forward reaction and one backward reaction in which case all fluxes can only take on positive values. Thus, a given position in the matrix describes the stoichiometric participation of a metabolite (listed in the given row) in a particular flux of interest (listed in the given column). Together all of the columns of the genome specific stoichiometric matrix represent all of the chemical conversions and cellular transport processes that are determined to be present in the organism. This includes all internal fluxes and so called exchange fluxes operating within the metabolic network. Thus, the process 50 moves to a state 58 in order to formulate all of the cellular reactions together in a genome specific stoichiometric matrix. The resulting genome specific stoichiometric matrix is a fundamental representation of a genomically and biochemically defined genotype. After the genome specific stoichiometric matrix is defined at state 58, the metabolic demands placed on the organism are calculated. The metabolic demands can be readily determined from the dry weight composition of the cell. In the case of well-studied organisms such as Escherichia coli and Bacillus subtilis, the dry weight composition is available in the published literature. However, in some cases it will be necessary to experimentally determine the dry weight composition of the cell for the organism in question. This can be accomplished with varying degrees of accuracy. The first attempt would measure the RNA, DNA, protein, and lipid fractions of the cell. A more detailed analysis would also provide the specific fraction of nucleotides, amino acids, etc. The process 50 moves to state 60 for the determination of the biomass composition of the target organism. The process 50 then moves to state 62 to perform several experiments that determine the uptake rates and maintenance requirements for the organism. Microbiological experiments can be carried out to determine the uptake rates for many of the metabolites that are transported into the cell. The uptake rate is determined by measuring the depletion of the substrate from the growth media. The measurement of the biomass at each point is also required, 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. Next, the process 50 moves to a state 64 wherein information regarding the metabolic demands and uptake rates obtained at state 62 are combined with the genome specific stoichiometric matrix of step 8 together fully define the metabolic system using flux balance analysis (FBA). This is an approach well suited to account for genomic detail as it has been developed based on the well-known stoichiometry of metabolic reactions. 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.Thus, the transient mass balances can be simplified to only consider the steady state behavior. Eliminating the time derivatives obtained from dynamic mass balances around every metabolite in the metabolic system, yields the system of linear equations represented in matrix notation, S • v - 0 Equation 1 where S refers to the stoichiometric matrix of the system, and v is the flux vector. This equation simply states that over long times, the formation fluxes of a metabolite must be balanced by the degradation fluxes. Otherwise, significant amounts of the metabolite will accumulate inside the metabolic network. Applying equation 1 to our system we let S now represent the genome specific stoichiometric matrix To determine the metabolic capabilities of a defined metabolic genotype Equation 1 is solved for the metabolic fluxes and the internal metabolic reactions, v, while imposing constraints on the activity of these fluxes. Typically the number of metabolic fluxes is greater than the number of mass balances (i.e., m > n) resulting in a plurality of feasible flux distributions that satisfy Equation 1 and any constraints placed on the fluxes of the system. This range of solutions is indicative of the flexibility in the flux distributions that can be achieved with a given set of metabolic reactions. The solutions to Equation 1 lie in a restricted region. This subspace defines the capabilities of the metabolic genotype of a given organism, since the allowable solutions that satisfy Equation 1 and any constraints placed on the fluxes of the system define all the metabolic flux distributions that can be achieved with a particular set of metabolic genes. The particular utilization of the metabolic genotype can be defined as the metabolic phenotype that is expressed under those particular conditions. Objectives for metabolic function can be chosen to explore the 'best' use of the metabolic network within a given metabolic genotype. The solution to equation 1 can be formulated as a linear programming problem, in which the flux distribution that minimizes a particular objective if found. Mathematically, this optimization can be stated as; Minimize Z Equation 2 where Z = _~ c t - v, = (c • v) Equation 3 where Z is the objective which is represented as a linear combination of metabolic fluxes vj. The optimization can also be stated as the equivalent maximization problem; i.e. by changing the sign on Z. This general representation of Z enables the formulation of a number of diverse objectives. 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. For this application, growth is to be defined in terms of biosynthetic requirements based on literature values of biomass composition or experimentally determined values such as those obtained from state 60. Thus, we can define biomass generation as an additional reaction flux draining intermediate metabolites in the appropriate ratios and represented as an objective function Z 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 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. It is analagous to adding an addition column to the stoichiometric matrix of Equation 1 to represent such a flux 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. Using linear programming, additional constraints can be placed on the value of any of the fluxes in the metabolic network. β j < f, < α, Equation 4 These constraints could be representative of a maximum allowable flux through a given reaction, possibly resulting from a limited amount of an enzyme present in which case the value for α, would take on a finite value. These constraints could also be used to include the knowledge of the minimum flux through a certain metabolic reaction in which case the value for β, would take on a finite value. 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 β, to negative infinity and α,to positive infinity. If reactions proceed only in the forward reaction β j is set to zero while α, is set to positive infinity. As an example, to simulate the event of a genetic deletion the flux through all of the corresponding metabolic reactions related to the gene in question are reduced to zero by setting β j and α, to be zero in Equation 4. Based on the in vivo environment where the bacteria lives one can determine the metabolic resources available to the cell for biosynthesis of essentially molecules for biomass. Allowing the corresponding transport fluxes to be active provides the in silico bacteria with inputs and ouputs for substrates and by-products produces by the metabolic network. Therefore as an example, if one wished to simulate the absence of a particular growth substrate one simply constrains the corresponding transport fluxes allowing the metabolite to enter the cell to be zero by allowing β, and α, to be zero in Equation 4. 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. Together the linear programming representation of the genome-specific stoichiometric matrix as in Equation 1 along with any general constraints placed on the fluxes in the system, and any of the possible objective functions completes the formulation of the in silico bacterial strain. The in silico strain can then be used to study theoretical metabolic capabilities by simulating any number of conditions and generating flux distributions through the use of linear programming. The process 50 of formulating the in silico strain and simulating its behavior using linear programming techniques terminates at an end state 66. Thus, by adding or removing constraints on various fluxes in the network it is possible to (1) simulate a genetic deletion event and (2) simulate or accurately provide the network with the metabolic resources present in its in vivo environment. Using flux balance analysis it is possible to determine the affects of the removal or addition of particular genes and their associated reactions to the composition of the metabolic genotype on the range of possible metabolic phenotypes. If the removal/deletion does not allow the metabolic network to produce necessary precursors for growth, and the cell can not obtain these precursors from its environment, the deletion(s) has the potential as an antimicrobial drug target. Thus by adjusting the constraints and defining the objective function we can explore the capabilities of the metabolic genotype using linear programming to optimize the flux distribution through the metabolic network. This creates what we will refer to as an in silico bacterial strain capable of being studied and manipulated to analyze, interpret, and predict the genotype-phenotype relationship. It can be applied to assess the affects of incremental changes in the genotype or changing environmental conditions, and provide a tool for computer aided experimental design. It should be realized that other types of organisms can similarly be represented in silico and still be within the scope of the invention. The construction of a genome specific stoichiometric matrix and in silico microbial strains can also be applied to the area of signal transduction. The components of signaling networks can be identified within a genome and used to construct a content matrix that can be further analyzed using various techniques to be determined in the future. Example 1: E. coli metabolic genotype and in silico model Using the methods disclosed in Figures 1 and 2, an in silico strain of Escherichia coli K-12 has been constructed and represents the first such strain of a bacteria largely generated from annotated sequence data and from biochemical information. The genetic sequence and open reading frame identifications and assignments are readily available from a number of on-line locations (ex: www.tigr.org). For this example we obtained the annotated sequence from the following website for the £ coli Genome Project at the University of Wisconsin (http:/lwww.geπetics.wisc.edu,). Details regarding the actual sequencing and annotation of the sequence can be found at that site. From the genome annotation data the subset of genes involved in cellular metabolism was determined as described above in Figure 1, state 20, comprising the metabolic genotype of the particular strain of £ coli. Through detailed analysis of the published biochemical literature on £ coli we determined (1) all of the reactions associated with the genes in the metabolic genotype and (2) any additional reactions known to occur from biochemical data which were not represented by the genes in the metabolic genotype. This provided all of the necessary information to construct the genome specific stoichiometric matrix for £ eσ//K-12. Briefly, the £ coli K-12 bacterial metabolic genotype and more specifically the genome specific stoichiometric matrix contains 731 metabolic processes that influence 436 metabolites (dimensions of the genome specific stoichiometric matrix are 436 x 731). There are 80 reactions present in the genome specific stoichiometric matrix that do not have a genetic assignment in the annotated genome, but are known to be present from biochemical data. The genes contained within this metabolic genotype are shown in Table 1 along with the corresponding reactions they carry out. Because £ coli is arguably the best studied organism, it was possible to determine the uptake rates and maintenance requirements (state 62 of Figure 2) by reference to the published literature. This in silico strain accounts for the metabolic capabilities of £ coli. It includes membrane transport processes, the central catabolic pathways, utilization of alternative carbon sources and the biosynthetic pathways that generate all the components of the biomass. In the case of £ coli KΛ2, we can call upon the wealth of data on overall metabolic behavior and detailed biochemical information about the in vivo genotype to which we can compare the behavior of the in silico strain. One utility of FBA is the ability to learn about the physiology of the particular organism and explore its metabolic capabilities without any specific biochemical data. This ability is important considering possible future scenarios in which the only data that we may have for a newly discovered bacterium (perhaps pathogenic) could be its genome sequence. Example 2: in silico deletion analysis for E. coli to find antimicrobial targets Using the in silico strain constructed in Example 1, the effect of individual deletions of all the enzymes in central metabolism can be examined in silico. For the analysis to determine sensitive linkages in the metabolic network of £ coli, the objective function utilized is the maximization of the biomass yield. This is defined as a flux draining the necessary biosynthetic precursors in the appropriate ratios. This flux is defined as the biomass composition, which can be determined from the literature. See Neidhardt et. al., Escherichia coli and Salmonella: Cellular and Molecular Biology, Second Edition, ASM Press, Washington D.C., 1996. Thus, the objective function is the maximization of a single flux, this biosynthetic flux. Constraints are placed on the network to account for the availability of substrates for the growth of £ coli. In the initial deletion analysis, growth was simulated in an aerobic glucose minimal media culture. Therefore, the constraints are set to allow for the components included in the media to be taken up. The specific uptake rate can be included if the value is known, otherwise, an unlimited supply can be provided. The uptake rate of glucose and oxygen have been determined for £ coli (Neidhardt et. al., Escherichia coli and Salmonella: Cellular and Molecular Biology. Second Edition, ASM Press, Washington D.C., 1 96. Therefore, these values are included in the analysis. The uptake rate for phosphate, sulfur, and nitrogen source is not precisely known, so constraints on the fluxes for the uptake of these important substrates is not included, and the metabolic network is allowed to take up any required amount of these substrates. The results showed that a high degree of redundancy exists in central intermediary metabolism during growth in glucose minimal media, which is related to the interconnectivity of the metabolic reactions. Only a few metabolic functions were found to be essential such that their loss removes the capability of cellular growth on glucose. For growth on glucose, the essential gene products are involved in the 3-carbon stage of glycolysis, three reactions of the TCA cycle, and several points within the PPP. Deletions in the 6-carbon stage of glycolysis result in a reduced ability to support growth due to the diversion of additional flux through the PPP. The results from the gene deletion study can be directly compared with growth data from mutants. The growth characteristics of a series of £ coli mutants on several different carbon sources were examined (80 cases were determined from the literature), and compared to the in silico deletion results (Table 2). The majority (73 of 80 cases or 91 %) of the mutant experimental observations are consistent with the predictions of the in silico study. The results from the in silico gene deletion analysis are thus consistent with experimental observations. Example 3: Prediction of genome scale shifts in gene expression Flux based analysis can be used to predict metabolic phenotypes under different growth conditions, such as substrate and oxygen availability. The relation between the flux value and the gene expression levels is non-linear, resulting in bifurcations and multiple steady states. However, FBA can give qualitative (on/off) information as well as the relative importance of gene products under a given condition. Based on the magnitude of the metabolic fluxes, qualitative assessment of gene expression can be inferred. Figure 3a shows the five phases of distinct metabolic behavior of £ Coli in response to varying oxygen availability, going from completely anaerobic (phase I) to completely aerobic (phase V). Figures 3b and 3c display lists of the genes that are predicted to be induced or repressed upon the shift from aerobic growth (phase V) to nearly complete anaerobic growth (phase II). The numerical values shown in Figures 3b and 3c are the fold change in the magnitude of the fluxes calculated for each of the listed enzymes. For this example, the objective of maximization of biomass yield is utilized (as described above). The constraints on the system are also set accordingly (as described above). However, in this example, a change in the availability of a key substrate is leading to changes in the metabolic behavior. The change in the parameter is reflected as a change in the uptake flux. Therefore, the maximal allowable oxygen uptake rate is changed to generate this data. The figure demonstrates how several fluxes in the metabolic network will change as the oxygen uptake flux is continuously decreased. Therefore, the constraints on the fluxes is identical to what is described in the previous section, however, the oxygen uptake rate is set to coincide with the point in the diagram. Corresponding experimental data sets are now becoming available. Using high-density oligonucieotide arrays the expression levels of nearly every gene in Saccharomyces cerevisiae can now be analyzed under various growth conditions. From these studies it was shown that nearly 90% of all yeast mRNAs are present in growth on rich and minimal media, while a large number of mRNAs were shown to be differentially expressed under these two conditions. Another recent article shows how the metabolic and genetic control of gene expression can be studied on a genomic scale using DNA microarray technology (Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale, Science, Vol. 278, October 24, 1997. The temporal changes in genetic expression profiles that occur during the diauxic shift in S. cerevisiae were observed for every known expressed sequence tag (EST) in this genome. As shown above, FBA can be used to qualitatively simulate shifts in metabolic genotype expression patterns due to alterations in growth environments. Thus, FBA can serve to complement current studies in metabolic gene expression, by providing a fundamental approach to analyze, interpret, and predict the data from such experiments. Example 4: Design of defined media An important economic consideration in large-scale bioprocesses is optimal medium formulation. FBA can be used to design such media. Following the approach defined above, a flux-balance model for the first completely sequenced free living organism, Haemophilus influenzae, has been generated. One application of this model is to predict a minimal defined media. It was found that H. influenzae can grow on the minimal defined medium as determined from the ORF assignments and predicted using FBA. Simulated bacterial growth was predicted using the following defined media: fructose, arginine, cγsteine, glutamate, putrescine, spermidine, thiamin, NAD, tetrapyrrole, pantothenate, ammonia, phosphate. This predicted minimal medium was compared to the previously published defined media and was found to differ in only one compound, inosine. It is known that inosine is not required for growth, however it does serve to enhance growth. Again the in silico results obtained were consistent with published in vivo research. These results provide confidence in the use of this type of approach for the design of defined media for organisms in which there currently does not exist a defined media. While particular embodiments of the invention have been described in detail, it will be apparent to those skilled in the art that these embodiments are exemplary rather than limiting, and the true scope of the invention is defined by the claims that follow. Table 1 The genes included in the £ coli metabolic genotype along with corresponding enzymes and reactions that comprise the genome specific stoichiometric matrix. The final column indicates presence/absence of the gene (as the number of copies) in the £ coli genome. Thus the presence of a gene in the £ coli genome indicates that the gene is part of the metabolic genoty Reactions/Genes not present in the genome are those gathered at state 56 in Figure 2 and together with the reactions of the genes in the metabolic genotype form the columns of the geno specific stoichiometric matrix. Enzyme Gene Reaction E. coli genome Glucokinase glk GLC + ATP -> G6P + ADP Glucokinase glk bOGLC + ATP -> bDG6P + ADP Phosphoglucose isomerase pgi G6P <-> F6P Phosphoglucose isomerase pgi bDG6P < -> G6P Phosphoglucose isomerase pgi bDG6P <-> F6P Aldose 1-epimerase galM bDGLC < -> GLC Glucose-1-phophatase agp G1P -> GLC + PI Phosphofructokinase pfkA F6P + ATP -> FDP + ADP Phosphofrtictokinase B pfkB F6P + ATP -> FDP + ADP Fructose- 1 ,6-bisphosphatase fbp FDP -> F6P + PI Fructose-1 ,6 bisphosphatate aldolase fba FDP <-> T3P1 + T3P2 Triosphosphate Isomerase tpiA T3P1 <- > T3P2 Methylglyoxal synthase mgsA T3P2 - > MTHGXL + PI Glyceraldehyde-3-phosphate dehydrogenase-A gapA T3P1 + PI + NAD <-> NADH + 13PDG complex Glyceraldehyde-3-phosprιate de ydrogenase-C gapC1C2 T3P1 + PI + NAD <-> NADH + 13PDG complex Enzyme Gene Reaction E. coli genome Phosphoglycerate kinase pgk 13PDG + ADP <-> 3PG + ATP 1 Phosphoglycerate mutase 1 gptnA 3PG <-> 2PG Phosphoglycerate mutase 2 gpmB 3PG <-> 2PG Enolase eπo 2PG <-> PEP Phosphoeπolpyruvate synthase ppsA PYR + ATP- > PEP + AMP + PI Pyruvate Kinase II pykA PEP + ADP-> PYR + ATP Pyruvate Kinase I pykF PEP + ADP-> PYR + ATP Pyruvate dehydrogenase IpdA, aceEF PYR + COA + NAD -> NADH + C02 + ACCOA Glucose- 1 -phosphate adenylytransferase glgC ATP + G1P-> ADPGLC + PPI Glycogen synthase glgA ADPGLC-> ADP + GLYCOGEN Glycogen phosphorylase glgP GLYCOGEN + PI -> G1P Maltodextrin phosphorylase malP GLYCOGEN + PI -> G1P Glucose 6-phosphate-1 -dehydrogenase zwf G6P + NADP <-> D6PGL+ NADPH 6-Phosphogluconolactonase pgi D6PGL - > D6PGC 6-Phosphogluconate dehydrogenase (decarboxylatiπg) gnd D6PGC + NADP -> NADPH + C02 + RL5P Ribose 5-phosphate isomerase A rpiA RL5P <-> R5P Ribose-5-phosphate isomerase B rpiB RL5P <-> R5P Ribulose phosphate 3-epimerase rpe RL5P <-> X5P Transketolase I tktA R5P + X5P <-> T3P1 + S7P Transketolase II tktB R5P + X5P <-> T3P1 + S7P Transketolase I tktA X5P + E4P <-> F6P + T3P1 Enzyme Gene Reaction E. coli genome Transketolase II tktB X5P + E4P <-> F6P + T3P1 Transaldolase B talB T3P1 + S7P <-> E4P + F6P Phosphogluconate dehydratase edd D6PGC-> 2KD6PG 2-Keto-3-deoxy-6 phosphogluconate aldolase eda 2KD6PG-> T3P1 + PYR Citrate synthase gltA ACCOA + 0A-> COA + CIT Acoπitase A acnA CIT <-> ICIT Aconitase B acnB CIT < ■ > ICIT Isocitrate dehydrogenase icdA ICIT + NADP <-> C02 + NADPH + AKG 2-Ketoglutarate dehyrogeπase sucAB, IpdA AKG + NAD + COA -> C02 + NADH + SUCCOA Succinyl CoA synthetase sucCD SUCCOA + ADP + PI <-> ATP + COA + SUCC Succinate dehydrogenase sdhABCD SUCC + FAD -> FADH + FUM Fumurate reductase frdABCD FUM + FADH-> SUCC + FAD Fumarase A fumA FUM <-> MAL Fumarase B fumB FUM <-> MAL Fumarase C fumC FUM <-> MAL Malate dehydrogenase mdh MAL + NAD <-> NADH + OA D-Lactate dehydrogenase 1 did PYR + NADH <-> NAD + LAC D-Lactate dehydrogenase 2 IdhA PYR + NADH <-> NAD + LAC Acetaldehyde dehydrogenase adhE ACCOA +2 ADH <-> ETH +2 NAD + COA Pyruvate formate lyase 1 pflAB PYR + C0A-> ACCOA + FOR Pyruvate formate lyase 2 pf/CB PYR + COA -> ACCOA + FOR Enzyme Gene Reaction E. coli genome Formate hydrogen lyase fdhF, hycBEFG F0R-> C02 5 Phosphotransacetylase pta ACCOA + PI <-> ACTP + COA Acetate kinase A ackA ACTP + ADP <-> ATP + AC GAR transformylase T purl ACTP + ADP <-> ATP + AC Acetyl-CoA synthetase acs ATP + AC + COA -> AMP + PPI + ACCOA Phosphoeπolpyruvatecarboxykiπase pckA 0A + ATP-> PEP + C02 + ADP Phosphoeπolpyruvate carboxylase ppc PEP + C02-> OA + PI Malic enzyme (NADP) maeB MAL + NADP -> C02 + NADPH + PYR Malic enzyme (NAD) sfcA MAL + NAD-> C02 + NADH + PYR Isocitrate lyase aceA ICIT-> GLX + SUCC Malate synthase A aceB ACCOA + GLX-> COA + MAL Malate synthase G glcB ACCOA + GLX-> COA + MAL Inorganic pyrophosphatase ppa PPI -> 2 PI NADH dehydrogenase II ndh NADH + Q-> NAD + QH2 NADH dehydrogenase 1 πuoABEFGHIJKLMN NADH + Q-> NAD + QH2+3.5HEXT Formate dehydrogenase-N fdnGHI F0R + Q-> 0H2 + C02 +2 HEXT Formate dehydrogenase-0 fdolHG F0R + Q-> QH2 + C02+2HEXT Formate dehydrogenase fdhF F0R + Q-> QH2 + C02+2HEXT Pyruvate oxidase poxB PYR + Q > AC + C02 + QH2 Glycerol-3-phosphate dehydrogenase (aerobic) glpD GL3P + Q-> T3P2 + QH2 Glycerol-3-phosphate dehydrogenase (anaerobic) glpABC GL3P + Q-> T3P2 + QH2 Enzyme Gene Reaction E. coli genome Cytochrome oxidase bo3 cyoABCD, cycBC, cydAB QH2+.502-> Q+2.5HEXT 6 Cγtochrome oxidase bd cydABCB, appBC, cycBC QH2+.502-> Q+2HEXT Succinate dehydrogenase complex sdhABCB FADH + Q <-> FAD + QH2 Thioredoxin reductasε trxB 0THI0 + NADPH -> NADP + RTHIO Pyridine nucleotide traπshydrogenase pntAB NADPH + NAD -> NADP + NADH Pyridine nucleotide traπshydrogenase pπtAB NADP + NADH +2HEXT-> NADPH + NAD Hydrogenase I hyaABC 2Q+2HEXT <-> 2QH2 + H2 Hydrogenase 2 hybAC 2Q+2HEXT <-> 2QH2 + H2 Hydrogenase 3 hycFGBE 2Q+2HEXT <-> 2QH2 + H2 FOFI-ATPase atpABCBEFGHI ATP <-> ADP + PI+4HEXT Alpha galactosidase (melibiase) me/A MELI-> GLC + GLAC Galactokinase galK GLAC + ATP-> GAL1P + ADP Galactose-1 -phosphate uridylyltransferase galT GAL1P + UDPG <-> G1P + UDPGAL UDP-glucose 4-epimerase galE UDPGAL <-> UDPG UDP-glucose-1 -phosphate uridylyltransferase galU G1P + UTP <-> UDPG + PPI Phosphoglucomutase pgm G1P <-> G6P Periplasmic beta-glucosidase precursor bglX LCTS-> GLC + GLAC Beta-galactosidase (LACTase) lacZ LCTS-> GLC + GLAC trehalose-6-phosphate hydrolase treC TRE6P-> bDG6P + GLC Beta-fructofuranosidase SUC6P-> G6P + FRU 1 -Phosphof ructokiπase fruK F1P + ATP-> FDP + ADP (Fructose 1 -phosphate kinase) Enzyme Gene Reaction E. coli genome Xylose isomerase xylA FRU - > GLC Phosphomaπnomutase cpsG MAN6P <-> MAN1P Mannose-6-phosphate isomerase maπA MAN1P <-> F6P N-Acetylglucosamine-6-phosphate deacetylase nagA NAGP-> GA6P + AC Glucosamine-6-phosphate deamiπase nagB GA6P-> F6P + NH3 N-Acetylneuramiπate lyase nanA SLA-> PYR + NAMAN L-Fucose isomerase fuel FUG <-> FCL L-Fuculokinase fucK FCL + ATP-> FCL1P + ADP L-Fuculose phosphate aldolase fucA FCL1P <-> LACAL + T3P2 Lactaldehyde reductase fucO LACAL + NADH <-> 12PPD + NAD Aldehyde dehydrogenase A aldA LACAL+NAD <-> LLAC + NADH Aldehyde dehydrogenase B aldB LACAL + NAD <-> LLAC + NADH Aldehyde dehydrogenase adhC LACAL + NAD <-> LLAC + NADH Aldehyde dehydrogenase adhC GLAL + NADH <-> GL + NAD Aldehyde dehydrogenase adhE LACAL + NAD-> LLAC + NADH Aldehyde dehydrogenase a/dH LACAL + NAD <-> LLAC + NADH Aldehyde dehydrogenase a/dH ACAL + NAD-> AC + NADH Gluconokinase I gntV GLCN + ATP-> D6PGC + ADP Gluconokiπase II gntK GLCN + ATP-> D6PGC + ADP L-Rhamnose isomerase rhaA RMN <-> RML Rhamnulokiπase rhaB RML + ATP-> RML1P + ADP Enzyme Gene Reaction E. coli genome Rhamnulose-1 -phosphate aldolase rhaD RML1P <-> LACAL + T3P2 L-Arabinose isomerase araA ARAB <-> RBL Arabinose-5-phosphate isomerase RL5P <-> A5P L-Ribulokiπase araB RBL + ATP-> RL5P + ADP L-Ribulose-phosphate 4-epimerase araB RL5P <-> X5P Xylose isomerase xylA XYL <-> XUL Xylulokinase xylB XUL + ATP-> X5P + ADP Ribokinase rbsK RIB + ATP-> R5P + ADP Mannitol- 1 -phosphate 5-dehydrogenase mtlD MNT6P + NAD <-> F6P + NADH Glucitol-6-phosphate dehydrogenase sr/B GLT6P + NAD <-> F6P + NADH Galactitol-1 -phosphate dehydrogenase gatB GLTL1P + NAD <-> TAG6P + NADH Phosphofructokinase B pfkB TAG6P + ATP-> TAG16P + ADP 1-Phosphofructokinase fruK TAG6P + ATP-> TAG16P + ADP Tagatose-6-phosphate kinase agaZ TAG6P + ATP-> TAG16P + ADP Tagatose-bisphosphate aldolase 2 gatY TAG16P <-> T3P2 + T3P1 Tagatose-bisphosphate aldolase 1 agaY TAG16P <-> T3P2 + T3P1 Glycerol kinase glpK GL + ATP-> GL3P + ADP Glycerol-3 phosphate dehydrogenase-[NAD(P)+] gpsA GL3P + NADP <-> T3P2 + NADPH Phosphopentomutase deoB DR1P <-> DR5P Phosphopentomutase deoB R1P <-> R5P Deoxyribose-phosphate aldolase deoC DR5P-> ACAL + T3P1 Enzyme Gene Reaction E. coli genome Asparate transaminase aspC OA + GLU <-> ASP + AKG 1 Asparagine synthetase (Glutamate dependent) asnB ASP + ATP + GLN -> GLU + ASN + AMP + PPI 1 Aspartate-ammonia ligase asnA ASP + ATP + NH3 - > ASN + AMP + PPI 1 Glutamate dehydrogenase gdhA AKG + NH3 + NADPH <•> GLU + NADP 1 Glutamate-am onia ligase glnA GLU + NH3 + ATP-> GLN + ADP + PI 1 Glutamate synthase gltBB AKG + GLN + NADPH -> NADP + 2 GLU 2 Alanine transaminase alaB PYR + GLU <-> AKG + ALA 0 Valiπe-pyruvate aminotraπsferase avtA 0IVAL+ ALA-> PYR + VAL 1 Alanine racemase, biosynthetic air ALA <-> DALA 1 Alanine racemase, catabolic dadX ALA-> DALA 1 N-Acetylglutamate synthase argA GLU + ACCOA -> COA + NAGLU 1 N-Acetylglutamate kinase argB NAGLU + ATP-> ADP + NAGLUYP 1 CD N-Acetylglutamate phosphate reductase argC NAGLUYP + NADPH <-> NADP + PI + NAGLUSAL 1 Acetylornithine transaminase argB NAGLUSAL + GLU <-> AKG + NAARON 1 Acetylornithine deacetylase argE NAAR0N-> AC + ORN 1 Carbamoyl phosphate synthetase carAB GLN +2 ATP + C02 - > GLU + CAP +2 ADP + PI 2 Ornithine carbamoyl transferase 1 argF ORN + CAP <-> CITR + PI 2 Ornithine carbamoyl transferase 2 argl ORN + CAP <-> CITR + PI 1 Ornithine transaminase ygjGH ORN + AKG-> GLUGSAL + GLU 2 Argininosucciπate synthase argG CITR + ASP + ATP -> AMP + PPI + ARGSUCC 1 Argininosuccinate lyase argH ARGSUCC <-> FUM + ARG 1 Enzyme Gene Reaction E. coli genome Arginine decarboxylase, biosynthetic speA ARG-> C02 + AGM Argiπiπe decarboxylase, degradative adi ARG-> C02 + AGM Agmatinase speB AGM-> UREA + PTRC Ornithine decarboxylase, biosynthetic speC 0RN-> PTRC + C02 Ornithine decarboxylase, degradative speF 0RN-> PTRC + C02 Adenosylmethionine decarboxylase speD SAM <-> DSAM + C02 Spermidiπe synthase speE PTRC + DSAM-> SPMD + 5MTA Methylthioadeπosine nucleosidase 5MTA-> AD + 5MTR 5-Methylthioribose kinase 5MTR + ATP-> 5MTRP + ADP 5-Methylthioribose- 1 -phosphate isomerase 5MTRP <-> 5MTR1P E-1 (Enolase-phosphatase) 5MTR1P-> DKMPP E-3 (Unknown) DKMPP-> F0R + KMB Tansamiπation (Unknown) KMB + GLN-> GLU + MET γ-Glutamyl kinase proB GLU + ATP-> ADP + GLUP Glutamate-5-semialdehyde dehydrogenase proA GLUP + NADPH -> NADP + PI + GLUGSAL N-Acetylornithine deacetylase argE NAGLUSAL -> GLUGSAL + AC Pyrroline-5-carboxylate reductase proC GLUGSAL + NADPH -> PRO + NADP Threonine dehydratase, biosynthetic ilvA THR-> NH3 + 0BUT Threonine dehydratase, catabolic tdcB THR-> NH3 + OBUT Acetohydroxybutanoate synthase I ilvBN 0BUT + PYR-> ABUT + C02 Acetohγdroxγbutanoate synthase II ilvG(12JM 0BUT + PYR-> ABUT + C02 Enzyme Gene Reaction E. coli genome Acetohydroxybutanoate synthase III ilvlH OBUT + PYR > ABUT + C02 2 Acetohydroxy Acid iso eroreductase ilvC ABUT + NADPH -> NADP + DHMVA 1 Dihγdroxy acid dehydratase ilvB DHMVA-> OMVAL 1 Branched chain amino acid aminotransferase ilvE OMVAL + GLU <-> AKG + ILE 1 Acetolactate synthase I ilvBN 2PYR > C02 + ACLAC 2 Acetolactate synthase II HvG(12jM 2PYR-> C02 + ACLAC 3 Acetolactate synthase III ilvlH 2PYR-> C02 + ACLAC 2 Acetohydroxy acid isomeroreductase ilvC ACLAC + NADPH -> NADP + DHVAL Dihydroxy acid dehydratase ilvB DHVAL-> OIVAL Branched chain amino acid aminotransferase ilvE OIVAL + GLU-> AKG + VAL Valinepyruvate aminotransferase avtA OIVAL + ALA- > PYR + VAL N-l Isopropylmalate synthase leuA ACCOA + OIVAL -> COA + CBHCAP ls-> Isopropylmalate isomerase leuCD CBHCAP <-> IPPMAL 3-lsopropylmalate dehydrogenase leuB IPPMAL + NAD -> NADH + OICAP + C02 Branched chain amino acid aminotransferase ilvE OICAP + GLU-> AKG + LEU Aromatic amino acid transaminase tyrB OICAP + GLU -> AKG + LEU 2-Dehydro-3-deoxyphosphoheptoπate aldolase F aroF E4P + PEP - > PI + 3DDAH7P 2-Dehydro-3-deoxyphosphoheptonate aldolase G aroG E4P + PEP-> PI + 3DDAH7P 2-Dehydro-3-deoxyphosphoheptonate aldolase H aroH E4P + PEP-> PI + 3DDAH7P 3-Dehydroquinate synthase aroB 3DDAH7P-> DOT + PI 3-Dehydroquinate dehydratase aroD DOT <-> DHSK Enzyme Gene Reaction E. coli genome Shikimate dehydrogenase aroE DHSK + NADPH <-> SME + NADP Shikimate kinase I aroK SME + ATP-> ADP + SME5P Shikimate kinase II aroL SME + ATP-> ADP + SME5P 3 Phosphoshikimate- 1 -carboxy vinyltransf erase am A SME5P + PEP <-> 3PSME + PI Chorismate synthase aroC 3PSME-> PI + CHOR Chorismate mutase 1 pheA CH0R-> PHEN Prephenate dehydratase pheA PHEN-> C02 + PHPYR Aromatic amino acid transaminase tyrB PHPYR + GLU <-> AKG + PHE Chorismate mutase 2 tyrA CHOR -> PHEN Prephanate dehydrogenase tyrA PHEN + NAD -> HPHPYR + C02 + NADH Aromatic amino acid transaminase tyrB HPHPYR + GLU <-> AKG + TYR Asparate transaminase aspC HPHPYR + GLU <-> AKG + TYR Aπthranilate synthase trpDE CHOR + GLN -> GLU + PYR + AN Anthranilate synthase component II trpD AN + PRPP-> PPI + NPRAN Phosphoribosyl anthranilate isomerase trpC NPRAN-> CPAD5P Iπdoleglycerol phosphate synthase trpC CPAD5P-> C02 + IGP Tryptophan synthase trpAB IGP + SER-> T3P1 + TRP Phosphoribosyl pyrophosphate synthase prsA R5P + ATP <-> PRPP + AMP ATP phosphoribosyltransferase hisG PRPP + ATP-> PPI + PRBATP Phosphoribosyl-ATP pyrophosphatase hislE PRBATP > PPI + PRBAMP Phosphoribosyl-AMP cyclohydrolase hislE PRBAMP > PRFP Enzyme Gene Reaction E. coli genome Phosphoribosylformimino-5-amino-1-phosphoribosyl- hisA PRFP - > PRLP 1 4-imidazole carboxamide isomerase Imidazoleglycerol phosphate synthase hisFH PRLP + GLN • > GLU + AICAR + DIMGP I idazoleglycerol phosphate dehydratase hisB DIMGP-> IMACP L-Histidinol phosphate aminotransferase hisC IMACP + GLU - > AKG + HISOLP Histidinol phosphatase hisB HIS0LP-> PI + HISOL Histidinol dehydrogenase hisB HIS0L + 3NAD-> HIS + 3 NADH 3-Pfιosphogfycerate dehydrogenase serA 3PG + NAD-> NADH + PHP Phosphoseriπe transaminase serC PHP + GLU-> AKG + 3PSER Phosphoserine phosphatase serB 3PSER - > PI + SER Glycine hydroxymethyltransferase glyA THF + SER-> GLY + METTHF Threonine dehydrogenase tdh THR + C0A-> GLY + ACCOA Amino ketobutyrate CoA ligase kbl THR + C0A-> GLY + ACCOA Sulfate adenylyltransferase cysBN SLF + ATP + GTP -> PPI + APS + GDP + PI Adenylylsulfate kinase cysC APS + ATP- > ADP + PAPS 3'Phospho-adenylylsulfatered-ιctase cysH PAPS + RTHIO -> OTHIO + H2S03 + PAP Sulfite reductase cyslJ H2S03 + 3 NADPH <-> H2S + 3 NADP Seriπe transacεtylase cysE SER + ACCOA <-> COA + ASER OAcetylsεrine (thiol)lyase A cysK ASER + H2S > AC + CYS O-Acetylseriπe (thiol)-lyase B cysM ASER + H2S-> AC + CYS 3' - 5' Bisphosphate nucleotidase PAP-> AMP + PI Enzyme Gene Reaction E. coli genome Aspartate kinase I thrA ASP + ATP <-> ADP + BASP Aspartate kinase II metl ASP + ATP <-> ADP + BASP Aspartate kinase III lysC ASP + ATP <-> ADP + BASP Aspartate semialdehyde dehydrogenase asd BASP + NADPH <-> NADP + PI + ASPSA Ho oserine dehydrogenase I thrA ASPSA + NADPH < • > NADP + HSER Homoserine dehydrogenase II metl ASPSA + NADPH < • > NADP + HSER Homoserine kinase thrB HSER + ATP -> ADP + PHSER Threonine synthase thrC PHSER-> PI + THR Dihydrodipicoliπate synthase dapA ASPSA + PYR -> D23PIC Dihydrodipicoliπate reductase dapB D23PIC + NADPH -> NADP + PIP26DX Tetrahydrodipicoliπate succiπylase dapB PIP26DX + SUCCOA -> COA + NS2A60 Succinyl diamiπopimelate aminotransferase dapC NS2A60 + GLU <•> AKG + NS26DP Succinyl diamiπopimelate desuccinylase dapE NS26DP-> SUCC + D26PIM Diaminopi elate epimerase dapF D26PIM <-> MDAP Diamiπopimelate decarboxylase lysA MDAP-> C02 + LYS Lysiπe decarboxylase 1 cadA LYS-> C02 + CADV Lysinε decarboxylase 2 IdcC LYS-> C02 + CADV Homoserine transsuccinylase metA HSER + SUCCOA -> COA + OSLHSER Osuccinlyhomoserine lyase etB OSLHSER + CYS-> SUCC + LLCT Cystathionine-βlyase metC LLCT > HCYS + PYR + NH3 Adenosyl homocysteiπase (Unknown) Unknown HCYS + ADN <-> SAH Enzyme Gene Reaction E. coli genome Cobalamin-dependent methionine synthase metH HCYS + MTHF-> MET + THF Cobalamin-independent methionine synthase etE HCYS + MTHF-> MET + THF S-Adeπosylmethioπine synthetase metK MET + ATP-> PPI + PI + SAM D-Amino acid dehydrogenase dadA DALA + FAD -> FADH + PYR + NH3 Putresciπe transaminase pat PTRC +AKG -> GABAL + GLU Amino oxidase tynA PTRC > GABAL + NH3 Amiπobutyraldehyde dehydrogenase prr GABAL + NAD-> GABA + NADH Aldehyde dehydrogenase a/dH GABAL + NAD-> GABA + NADH Aminobutyrate amiπotransaminase 1 gabT GABA + AKG-> SUCCSAL + GLU Amiπobutyrate aminotransa inase 2 goaG GABA + AKG- > SUCCSAL + GLU Succiπate semialdehyde dehydrogenase -NAD sad SUCCSAL + NAD-> SUCC + NADH Succinate semialdehyde dehydrogenase -NADP gabB SUCCSAL + NADP-> SUCC + NADPH Asparininase I ansA ASN-> ASP + NH3 Asparininase II ansB ASN-> ASP + NH3 Aspartate ammonia-lyase aspA ASP-> FUM + NH3 Tryptophaπase tnaA CYS-> PYR + NH3 + H2S L-Cysteine desulfhγdrase CYS-> PYR + NH3 + H2S Glutamate decarboxylase A gadA GLU-> GABA + C02 Glutamate decarboxylase B gadB GLU-> GABA + C02 Glutamiπase A GLN-> GLU + NH3 Glutamiπase B GLN-> GLU + NH3 Enzyme Gene Reaction E. coli genome Proline dehydrogenase putA PRO + FAD-> FADH + GLUGSAL Pyrroliπe-5-carboxylate dehydrogenase putA GLUGSAL + NAD -> NADH + GLU Serine dea iπase 1 sdaA SER-> PYR + NH3 Serine deamiπase 2 sdaB SER-> PYR + NH3 Trypothanase tnaA SER-> PYR + NH3 D-Serine deaminase dsdA DSER-> PYR + NH3 Threonine dehydrogenase tdh THR + NAD-> 2A30 + NADH Amino ketobutyrate ligase kbl 2A30 + C0A-> ACCOA + GLY Threonine dehydratase catabolic tdcB THR-> 0BUT + NH3 Threonine deaminase 1 sdaA THR-> 0BUT + NH3 Threonine deaminase 2 sdaB THR-> 0BUT + NH3 Tryptophanase tnaA TRP <-> INDOLE + PYR + NH3 Amidophosphoribosyl transferase purF PRPP + GLN > PPI + GLU + PRAM Phosphoribosylamine-glycine ligase purB PRAM + ATP + GLY < • > ADP + PI + GAR Phosphoribosylglycina ide formyltransferase pυrN GAR + FTHF-> THF + FGAR GAR transformylase T purT GAR + FOR + ATP -> ADP + PI + FGAR Phosphoribosylformylglycinamide synthetase pud FGAR + ATP + GLN -> GLU + ADP + PI + FGAM Phosphoribosylformylglycinamide cyclo-ligase purM FGAM + ATP • > ADP + PI + AIR Phosphoribosylamiπoimidazole carboxylase 1 purK AIR + C02 + ATP <-> NCAIR + ADP + PI Phosphoribosylaminoimidazole carboxylase 2 purE NCAIR <-> CAIR Phosphoribosylaminoimidazole-succiπocarboxamide purC CAIR + ATP + ASP <-> ADP + PI + SAICAR synthetase Enzyme Gene Reaction E. coli genome 5'-Phosphoribosyl4-(N-succinocarboxamide)-5- purB SAICAR <-> FUM + AICAR 1 amiπoimidazole lyase AICAR transformylase purH AICAR + FTHF <-> THF + PRFICA IMP cyclohydrolase purH PRFICA <-> IMP Adenylosuccinate synthetase purA IMP + GTP + ASP -> GDP + PI + ASUC Adenγlosuccinate lyase purB ASUC <-> FUM + AMP IMP dehydrogenase guaB IMP + NAD-> NADH + XMP GMP synthase guaA XMP + ATP + GLN -> GLU + AMP + PPI + GMP GMP reductase guaC GMP + NADPH -> NADP + IMP + NH3 Aspartate-carbamoyltransf erase pyrBI CAP + ASP- > CAASP + PI Dihydroorotasε pyrC CAASP <-> DOROA Dihydroorotate dεhydrogεnasε pyrD D0R0A + Q <-> QH2 + 0R0A Orotatε phosphoribosyl transfεrase pyrE OROA + PRPP <-> PPI + OMP OMP decarboxylase pyrF 0MP-> C02 + UMP CTP synthεtasε p rG UTP + GLN + ATP -> GLU + CTP + ADP + PI Adεπylatε kinasε adk ATP + AMP <-> 2 ADP Adenγlate kinase adk GTP + AMP <-> ADP + GDP Adenylate kinase adk ITP + AMP <-> ADP + IDP Adeπylate kinase adk DAMP + ATP <-> ADP + DADP Guanylate kinase gmk GMP + ATP <-> GDP + ADP Deoxyguanylate kinase gmk DGMP + ATP <-> DGDP + ADP Enzyme Gene Reaction E. coli genome Nucleoside-diphosphate kinase ndk GDP + ATP <-> GTP + ADP Nucleoside-diphosphate kinase ndk UDP + ATP <-> UTP + ADP Nucleoside-diphosphate kinase ndk CDP + ATP <-> CTP + ADP Nucleoside-diphosphate kinase ndk DGDP + ATP <-> DGTP + ADP Nucleoside-diphosphate kinase ndk DUDP + ATP <-> DUTP + ADP Nucleoside-diphosphate kinase ndk DCDP + ATP <-> DCTP + ADP Nucleoside-diphosphate kinase ndk DADP + ATP <-> DATP + ADP Nucleoside-diphosphate kinase ndk DTDP + ATP <-> DTTP + ADP AMP Nucleosidse amn AMP-> AD + R5P Adenosine deaminase add ADN-> INS + NH3 Deoxyadenosine deaminase add DA-> DIN + NH3 NJ Adeniπe deaminase yicP AD-> NH3 + HYXN O Inosine kinase gsk INS + ATP- > IMP + ADP Guaπosine kinase gsk GSN + ATP-> GMP + ADP Adenosine kinase adk ADN + ATP-> AMP + ADP Adenine phosphoryltraπsferase apt AD + PRPP-> PPI + AMP Xanthine-guanine phosphoribosyltransferase gpt XAN + PRPP > XMP + PPI Xanthine-guanine phosphoribosyltransferase gpt HYXN + PRPP-> PPI + IMP Hypoxanthine phosphoribosyltransferase hpt HYXN + PRPP-> PPI + IMP Xanthine-guanine phosphoribosyltransferase gpt GN + PRPP-> PPI + GMP Hypoxanthine phosphoribosyltransferase hpt GN + PRPP-> PPI + GMP Enzyme Gene Reaction E. coli genome Xanthosine phosphorylase xapA DIN + PI <-> HYXN + DR1P Purine nucleotide phosphorylase deoB DIN + PI <-> HYXN + DR1P Xanthosine phosphorylase xapA DA + PI <-> AD + DR1P Purine nucleotide phosphorylase deoB DA + PI <-> AD + DR1P Xanthosine phosphorylase xapA DG + PI <-> GN + DR1P Purine nucleotide phosphorylase deoD DG + PI <-> GN + DR1P Xanthosine phosphorylase xapA HYXN + R1P <-> INS + PI Purine nucleotide phosphorylase deoB HYXN + R1P <-> INS + PI Xanthosine phosphorylase xapA AD + R1P <-> PI + ADN Purine nucleotide phosphorylase deoB AD + R1P <-> PI + ADN Xanthosine phosphorylase xapA GN + R1P <-> PI + GSN Purine nucleotide phosphorylase deoD GN + R1P <-> PI + GSN Xanthosine phosphorylase xapA XAN + R1P <-> PI + XTSN Purine nucleotide phosphorylase deoB XAN + R1P <-> PI + XTSN Uridine phosphorylase udp URI + PI <-> URA + R1P Thymidine (deoxyuridine) phosphorylase deoA DU + PK-> URA + DR1P Purine nucleotide phosphorylase deoB DU + PI <-> URA + DR1P Thymidine (deoxyuridine) phosphorylase deoA DT + PI <-> THY + DR1P Cytidylate kinase cmkA DCMP + ATP <-> ADP + DCDP Cytidylate kinase ctnkA CMP + ATP <-> ADP + CDP Cytidylate kinase cmkB DCMP + ATP <-> ADP + DCDP Enzyme Gene Reaction E. coli genome Cytidylate kinase cmkB CMP + ATP <-> ADP + CDP Cytidylate kinase cmkA UMP + ATP <-> ADP + UDP Cytidylate kinase cmkB UMP + ATP <-> ADP + UDP dTMP kinase tmk DTMP + ATP <-> ADP + DTDP Uridylate kinase pyrH UMP + ATP <-> UDP + ADP Uridylate kinase pyrH DUMP + ATP <-> DU0P + ADP Thymidine (deoxyuridine) kinase tdk DU + ATP-> DUMP + ADP Uracil phosphoribosyltransferase upp URA + PRPP-> UMP + PPI Cytosine deamiπase codA CYTS-> URA + NH3 Uridine kinase udk URI + GTP-> GDP + UMP Cytodine kinase udk CYTD + GTP-> GDP + CMP CMP glycosylase CMP-> CYTS + R5P Cγtidiπe deamiπase cdd CYTD-> URI + NH3 Thymidine (deoxyuridine) kinase tdk DT + ATP-> ADP + DTMP dCTP deaminase dcd DCTP-> DUTP + NH3 Cytidiπe deaminase cdd DC-> NH3 + DU 5'-Nucleotidase ushA DUMP-> DU + PI δ'-Nucleotidase ushA DTMP > DT + PI 5' ucleotidase ushA DAMP-> DA + PI 5'Nucleotidase ushA DGMP-> DG + PI 5'-Nucleotidase ushA DCMP-> DC + PI Enzyme Gene Reaction E. coli genome 5'Nucleotidase ushA CMP-> CYTD + PI 1 5'Nucleotidase ushA AMP-> PI + ADN 1 δ'-Nucleotidase ushA GMP-> PI + GSN 1 5'Nucleotidase ushA IMP-> PI + INS 1 5'Nucleotidase ushA XMP-> PI + XTSN 1 5'Nucleotidase ushA UMP-> PI + URI 1 Ribonucleoside-diphosphate reductase nrdAB ADP + RTHIO -> DADP + OTHIO 2 Riboπucleoside-diphosphate reductase nrdAB GDP + RTHIO -> DGDP + OTHIO 2 Ribonucleoside-triphosphate reductase nrdB ATP + RTHIO -> DATP + OTHIO 1 Riboπucleoside-triphosphate reductase nrdB GTP + RTHIO -> DGTP + OTHIO 1 Ribonucleoside-diphosphate reductase nrdAB CDP + RTHIO -> DCDP + OTHIO 2 Ribonucleoside-diphosphate reductase II nrdEF CDP + RTHIO -> DCDP + OTHIO 2 Ribonucleoside-diphosphate reductase nrdAB UDP + RTHIO -> DUDP + OTHIO 2 Ribonuclεoside-triphosphate reductase nrdD CTP + RTHIO •> DCTP + OTHIO Ribonucleoside-triphosphate reductase nrdB UTP + RTHIO -> OTHIO + DUTP dUTP pyrophosphatase dut DUTP-> PPI + DUMP Thymidilate synthetase thy A DUMP + METTHF-> DHF + DTMP Nucleosidε triphosphatase mutT GTP-> GSN + 3PI Nucleoside triphosphatase mutT DGTP-> DG + 3PI Deoxyguaπosinetriphosphate triphophohydrolase dgt DGTP-> DG + 3PI Deoxyguanosinetriphosphate triphophohydrolase dgt GTP-> GSN + 3PI Enzyme Gene Reaction E. coli genome Glycine cleavage system gcvHTP, IpdA GLY + THF + NAD -> METTHF + NADH + C02 + NH3 (Multi-component system) Formyl tetrahydrofolate def ormylase purl] FTHF-> FOR + THF Methyleπe tetrahydrofolate reductase metF METTHF + NADH -> NAD + MTHF Methγlene THF dehydrogenase fo/B METTHF + NADP <-> METHF + NADPH Methenγl tetrahydrofolate cyclehydrolase MB METHF <-> FTHF Acetyl-CoA carboxyltransferase accABD ACCOA + ATP + C02 <-> MALCOA + ADP + PI Malonyl-CoA-ACP transacylase fabB MALCOA + ACP <-> MALACP + COA Malonyl-ACP decarboxylase fadB MALACP-> ACACP + C02 Acetyl-CoA-ACP transacylase fabH ACACP + COA <-> ACCOA + ACP Acyltransferase pis GL3P +0.035 C140ACP +0.102 C141ACP +0.717 C160ACP +0.142 C161ACP +1.004 C181ACP-> 2 ACP + PA CDP-Diacylglycerol synthetase cdsA PA + CTP <-> CDPDG + PPI CDP-Diacylglycerol pyrophosphatase cdh CDPDG-> CMP + PA Phosphatidylseriπe synthase pssA CDPDG + SER < > CMP + PS Phosphatidylserine decarboxylase psd PS-> PE + C02 Phosphatidylglycerol phosphate synthase pgsA CDPDG + GL3P <-> CMP + PGP Phosphatidylglycerol phosphate phosphatase A pgpA PGP-> PI + PG Phosphatidylglycerol phosphate phosphatase B pgpB PGP-> PI + PG Cardiolipin synthase c/s 2PG <-> CL + GL Acetyl-CoA C-acetyltransferase atoB 2 ACCOA <-> C0A + AACCOA Isopreπyl pyrophosphate synthesis pathway T3P1 + PYR + 2 NADPH + ATP -> IPPP + ADP + 2 NADP + C02 Enzyme Gene Reaction E. coli genome Isoprenyl pyrophosphate isomerase IPPP-> DMPP 0 Farnesyl pyrophosphate synthetasε ispA DMPP + IPPP-> GPP + PPI Gεranyltranstransfεrasε ispA GPP + IPPP-> FPP + PPI Octoprεnyl pyrophosphatε synthase (5 reactions) ispB 5 IPPP + FPP-> 0PP + 5PPI Uπdecaprenyl pyrophosphatε syπthasε (8 rεactions) 8 IPPP + FPP-> UDPP + 8PPI Chorismate pyruvate-lyasε ubiC CHOR -> 4HB2 + PYR Hγdroxγbenzoate octapreπyltraπsferase ubiA 4HBZ + 0PP-> 04HBZ + PPI Octaprenyl-hydroxybenzoate decarboxylase ubiB, ubiX 04HBZ-> C02 + 20PPP 2-Octaprenylpheπol hγdroxγlase ubiB 20PPP + 02-> 206H Methylation reaction 206H + SAM-> 20PMP + SAH 2-Octaprenyl-6-methoxyphεnolhydroxylase ubiH 20PMP + 02-> 20PMB 2-Octaprenyl-6-methoxy-1J-bεnzoquinone ubiE 20PMB + SAM -> 20PMMB + SAH methylase 2-0ctaprenyl-3-methγl-6-me-hoxy- 1 ,4-benzoquinonε ubiF 20PMMB + 02-> 20MHMB hydroxylasε 3-Dimethylubiquinone 3-methyltransferase ubiG 20MHMB + SAM-> QH2 + SAH Isochorismate synthase 1 menF CHOR -> ICHOR -Ketoglutarate decarboxylase menu AKG + TPP-> SSALTPP + C02 SHCHC synthase menu ICHOR + SSALTPP -> PYR + TPP + SHCHC O-Succinylbenzoatε-CoA synthase enC SHCHC -> OSB O-Succinylbenzoic acid-CoA ligase menE OSB + ATP + COA -> OSBCOA + AMP + PPI Naphthoatε synthasε menB 0SBC0A-> DHNA + COA Enzyme Gene Reaction E. coli genome 1 J-Dihydr oxy-2-napht hoate octaprεnyltransf erase menA DHNA + 0PP-> DMK + PPI + C02 S-Adεnosylmεthioninε-2-DMK ethyltransferase menG DMK + SAM > MK + SAH Isochorismate synthase 2 entC CH0R-> ICHOR Isochorismatase entB ICHOR <-> 23DHDHB + PYR 2,3-Dihydo-2,3-dihydroxybenzoate dehydrogenase entA 23DHDHB + NAD <-> 23DHB + NADH ATP-dependent activation of 2,3-dihydroxybeπzoate entE 23DHB + ATP <-> 23DHBA + PPI ATP-dependεnt serine activating enzyme entF SER + ATP <-> SERA + PPI Entεrocheliπ synthetase entB 3SERA+323DHBA-> ENTER +6 AMP GTP cyclohydrolase II ribA GTP-> D6RP5P + FOR + PPI Pryimidine deaminase ribB D6RP5P-> A6RP5P + NH3 Pyr imidine reductase ribB A6RP5P + NADPH -> A6RP5P2 + NADP Pγrimidine phosphatase A6RP5P2-> A6RP + PI 3,4 Dihydroxy-2-butanone-4-phosphatε synthasε ribB RL5P-> DB4P + F0R 6,7-Dimεthyl-8-ribityllumazine synthase ribF DB4P + A6RP-> D8RL + PI Riboflavin synthase ribH 2D8RL-> RIBFLV + A6RP Riboflavin kinase ribF RIBFLV + ATP - > FMN + ADP FAD synthεtasε ribF FMN + ATP-> FAD + PPI GTP cyclohydrolase 1 folE GTP-> FOR + AHTD Dihydroneopteriπ triphosphate pyrophosphorylasε ntpA AHTD-> PPI + DHPP Nuclεoside triphosphatase mutT AHTD-> DHP+3PI Dihydroneopteriπ monophosphate dephosphorylasε DHPP-> DHP + PI Enzyme Gene Reaction E. coli genome Dihydroneopterin aldolase MB DHP-> AHHMP + GLAL 1 6 Hydroxymethyl-7,8 dihydropterin K AHHMP + ATP-> AMP + AHHMD 1 pγrophosphokiπase A iπodeoxychorismate synthase pabAB CHOR + GLN -> ADCHOR + GLU Aminodeoxychorismatε iyasε pabC ADCH0R-> PYR + PABA Dihydropteroate synthase MP PABA + AHHMD-> PPI + DHPT Dihydrofolate synthetasε C DHPT + ATP + GLU -> ADP + PI + DHF Dihydrof olate reductase MA DHF + NADPH -> NADP + THF Ketopeπtoate hydroxy εthyl transferase panB OIVAL + METTHF -> AKP + THF Ketopaπtoatε reductase panE AKP + NADPH -> NADP + PANT Acetohyoxyacid isomεrorεductase ilvC AKP + NADPH -> NADP + PANT Aspartate decarboxylase panB ASP-> C02 + bALA Pantoate-β-alanine ligase panC PANT + bALA + ATP -> AMP + PPI + PNTO Paπtothenatε kinase coaA PNTO + ATP -> ADP + 4PPNT0 Phosphopantothenatε-cysteine ligase 4PPNT0 + CTP + CYS -> CMP + PPI + 4PPNCYS Phosphopantothenate-cysteiπedεcarboxylase 4PPNCYS-> C02 + 4PPNTE Phospho pantethiene adenylyltransferase 4PPNTE + ATP-> PPI + DPCOA DεphosphoCoA kinasε DPC0A + ATP-> ADP + COA ACP Synthase acpS C0A-> PAP + ACP Aspartatε oxidasε πadB ASP + FAD-> FADH + ISUCC Quinolate synthase nadA ISUCC + T3P2-> PI + QA Enzyme Gene Reaction E. coli genome Quiπolatε phosphoribosyl transferase nadC QA + PRPP-> NAMN + C02 + PPI 1 NAMN adenylyl transferase nadB NAMN + ATP-> PPI + NAAD 0 NAMN adenylyl transferase nadB NMN + ATP-> NAD + PPI 0 Deamido-NAD ammonia ligase nadE NAAD + ATP + NH3 -> NAD + AMP + PPI 1 NAD kinase nadFG NAD + ATP-> NADP + ADP 0 NADP phosphatase NADP-> NAD + PI 0 DNA ligase iig NAD-> NMN + AMP 1 NMN amidohydrolase pncC NMN-> NAMN + NH3 0 NMN glycohydrolase (cytoplasmic) NMN-> R5P + NAm 0 NAm amidohydrolase pncA NAm-> NAC + NH3 0 NAPRTase pncB NAC + PRPP + ATP -> NAMN + PPI + PI + ADP 1 NAD pyrophosphatasε pnuE NADxt-> NMNxt + AMPxt 0 NMN pεrmεase pnuC NMNxt-> NMN 1 NMN glycohydrolase (membrane bound) NMNxt-> R5P + NAm 0 Nicotinic acid uptake NACxt-> NAC 0 GSA synthetasε hemM GLU + ATP-> GTRNA + AMP + PPI 1 Glutamyl tRNA synthεtasε gltX GLU + ATP > GTRNA + AMP + PPI 1 Glutamyl-tRNA reductase hemA GTRNA + ADPH -> GSA + NADP 1 Glutama te- 1 -semialdehydε aminotransfεrasε hemL GSA-> ALAV 1 Porphobilinogεn synthase hemB 8ALAV-> 4PBG 1 Hydroxymethγlbilane synthase hemC 4PBG-> HMB + 4NH3 1 Enzyme Gene Reaction E. coli genome Uroporphyrinogen III synthase hemD HMB-> UPRG Uroporphyrin-lll C-methyltransfεrase 1 hemX SAM + UPRG-> SAH + PC2 Uroporphyrin-lll C-methyltransferase 2 cysG SAM + UPRG-> SAH + PC2 1,3-Dimεthyluroporphyrinogen III dehydrogεnase cysG PC2 + NAD-> NADH + SHCL Sirohεme f errochelatasε cysG SHCL-> SHEME Uroporphyrinogen decarboxylase hemE UPRG-> 4C02 + CPP Coproporphyrinogen oxidase, aerobic he F 02 + CPP-> 2C02 + PPHG Protoporphyrinogεn oxidasε he G 02 + PPHG-> PPIX Ferrochelatase he H PPIX-> PTH Heme 0 synthase cyoE PTH + FPP-> HO + PPI 8-Amino-7-oxononanoatε synthasε bioF ALA + CHCOA <-> C02 + COA + AONA Adenosylmεthioniπe-8-amino-7-oxonoπanoate bioA SAM + AONA <-> SAMOB + DANNA aminotransferase Dethiobiotiπ synthase bt ' oB C02 + DANNA + ATP <-> DTB + PI + ADP Biotin synthase bioB DTB + CYS < • > BT Glutamate-cysteine ligase gshA CYS + GLU + ATP-> GC + PI + ADP Glutathione synthase gshB GLY + GC + ATP-> RGT + PI + ADP Glutathionε reductase gor NADPH + 0GT <-> NADP + RGT thiC protein thiC AIR-> AHM HMP kinase thifll AHM + ATP > AHMP + ADP HMP-phosphate kinase MB AHMP + ATP-> AHMPP + ADP Enzyme Gene Reaction E. coli genome Hypothetical T3P1 + PYR-> DTP 0 thiG protein thiG DTP + TYR + CYS -> THZ + HBA + C02 1 thiE protein thiE DTP + TYR + CYS -> THZ + HBA + C02 1 thiF protein thiF DTP + TYR + CYS - > THZ + HBA + C02 1 thiH protein thiH DTP + TYR + CYS -> THZ + HBA + C02 1 THZ kinase thiM THZ + ATP-> THZP + ADP 0 Thiamin phosphate synthase thiB THZP + AHMPP-> THMP + PPI 0 Thiamiπ kinase thiK THMP + ADP <-> THIAMIN + ATP 0 Thiamin phosphate kinase thiL THMP + ATP <-> TPP + ADP 0 Erythrose 4-phosphate dehydrogenase epd E4P + NAD <-> ER4P + NADH 1 Eryth.oπate-4-phosphate dehydrogenase pdxB ER4P + NAD <-> 0HB + NADH 1 Hypothetical traπsamiπasefphosphoserine serC . . OHB + GLU <-> PHT + AKG 1 transaminase Pγridoxal-phosphate biosynthetic proteins pdxAJ PHT + DX5P-> P5P + C02 pdxJ-pdxA Pyridoxine 5'-phosphate oxidase pdxH P5P + 02 <-> PL5P + H202 Threonine synthase thrC PHT - > 4HLT + PI Hypothetical Enzyme 4HLT-> PYRDX Pyridoxine kinase pdxK PYRDX + ATP-> P5P + ADP Hypothetical Enzyme P5P - > PYRDX + PI Hypothetical Enzyme PL5P - > PL + PI Pyridoxine kinase pdxK PL + ATP-> PL5P + ADP Enzyme Gene Reaction E. coli genome Pyridoxine 5'phosphate oxidase pdxH PYRDX + 02 <-> PL + H202 Pyridoxine 5'phosphate oxidase pdxH PL + 02 + NH3 <-> PDLA + H202 Pyridoxine kiπasε pdxK PDLA + ATP-> PDLA5P + ADP Hypothεtical Enzyme PDLA5P - > PDLA + PI Pyridoxine 5'phosphate oxidase pdxH PDLA5P + 02 - > PL5P + H202 + NH3 Serine hydroxγmethyltransfεrase (serine methylasε) glyA PL5P + GLU-> PDLA5P + AKG Sεrinε hydroxymεthyltransferase (serine methylase) glyA PL5P + ALA-> PDLA5P + PYR Glutamine f ructose-6-phosphatε Transaminasε glmS F6P + GLN-> GLU + GABP Phosphoglucosamine mutase gimM GA6P <-> GA1P N-Acetylglucosamine-1 -phosphate-uridyltraπsf erasε glmU UTP + GA1P + ACCOA -> UDPNAG + PPI + COA UDP-N-acεtylglucosamine acyltransferase IpxA C140ACP + UDPNAG -> ACP + UDPG2AA UDP-3-O-acyl-N-acetylglucosamine deacetylase IpxC UDPG2AA-> UDPG2A + AC UDP-3-0-(3-hydroxymyristoyl)glucosamiπe- IpxB UDPG2A + C140ACP-> ACP + UDPG23A acyltransferase UDP-sugar hydrolase ushA UDPG23A-> UMP + LIPX Lipid A disaccharidε synthase IpxB LIPX + UDPG23A -> UDP + DISAC1P Tetraacyldisaccharide 4' kinase DISAC1P + ATP-> ADP + LIPIV 3-Deoxy-D-manno-octulosonic-acid transfεrasε kdtA LIPIV + CMPKDO -> KDOLIPIV + CMP (KDO transferase) 3-Deoxγ-D-manno-octulosonic-acid transfεrasε kdtA KDOLIPIV + CMPKDO -> K2LIPIV + CMP (KDO traπsfεrase) Endotoxin synthase htrB, msbB K2LIPIV + C140ACP + C120ACP • > LIPA +2 ACP Enzyme Gene Reaction E. coli genome 3-Deoxy-D-manno-octulosonic-acid 8-phosphate kdsA PEP + A5P-> KDOP + PI 1 synthase 3-Deoxy-D-manno-octulosonic-acid 8-phosphate KD0P-> KDO + PI phosphatase CMP-2-kεto-3-dεoxyoctonate synthesis kdsB KD0 + CTP-> PPI + CMPKDO ADP-L-glycero-D-mannoheptosε-6-εpimerase IpcA, rfaEB S7P + ATP-> ADPHEP + PPI UDP glucose- 1 -phosphate u ridylylt raπsf erase galU, galF G1P + UTP-> PPI + UDPG Ethanolamiπe phosphotraπsfεrasε PE + CMP <-> CDPET + DGR Phosphatidatε phosphatase PA-> PI + DGR Diacylglycerol kinase dgkA DGR + ATP-> ADP + PA LPS Synthesis - truncated version of LPS rMdlGFC LIPA +3 ADPHEP +2 UDPG +2 CDPETN + 3 CMPKDO -> LPS +3 ADP +2 UDP +3 CMP +2 CDP (ref neid) UDP-N-acεtylglucosaminε-εnolpyruvatεtransfεrase murA UDPNAG + PEP - > UDPNAGEP + PI UDP-N-acεtylglucosaminε-εnolpyruvatε murB UDPNAGEP + NADPH -> UDPNAM + NADP dehydrogenase UDP-N-acetylmuramate-alanine ligase murC UDPNAM + ALA + ATP -> ADP + PI + UDPNAMA UDP-N-acetylmuramoylalaninε-D-glutamatε ligase urB UDPNAMA + DGLU + ATP -> UDPNAMAG + ADP + PI UDP-N-acetylmuramoylalanyl-D-glutamate2,6- mυrE UDPNAMAG + ATP + MDAP -> UNAGD + ADP + PI diaminopimelatε ligase D-Alanine D-alanine adding enzyme mυrF UNAGD + ATP + AA • > UNAGDA + ADP + PI Glutamate racemase murl GLU <-> DGLU D-ala.D-ala ligases ddlAB 2 DALA <-> AA Enzyme Gene Reaction E. coli genome Phospho-N-acetylmuramoylpeπtapeptide mraY UNAGDA-> UMP + PI + UNPTDO 1 transferase N-Acetylglucosaminγl transfεrase murG UNPTDO + UDPNAG -> UDP + PEPTIDO Arabiπose (low affinity) araE ARABxt + HEXT <-> ARAB Arabiπose (high affinity) araFGH ARABxt + ATP -> ARAB + ADP + PI Dihydroxyacεtonε DHAxt + PEP-> T3P2 + PYR Fructosε fruABF FRUxt + PEP-> F1P + PYR Fucose fucP FUCxt + HEXT <-> FUC Galacitol gatABC GLTLxt + PEP-> GLTL1P + PYR Galactose (low affinity) galP GLACxt + HEXT-> GLAC Galactosε (low affinity) galP GLCxt + HEXT-> GLC Galactose (high affinity) mglABC GLACxt + ATP-> GLAC + ADP + PI Glucitol srlA1A2B GLTxt + PEP - > GLT6P + PYR Gluconate gntST GLCNxt + ATP-> GLCN + ADP + PI Glucose ptsG, err GLCxt + PEP - > G6P + PYR Glycerol gip GLxt <-> GL Lactose lacY LCTSxt + HEXT <-> LCTS Maltose malX, err, malEFGK, la B MLTxt + PEP -> MLT6P + PYR Mannitol mtlA, cmtAB MNTxt + PEP-> MNT6P + PYR Mannose manXYZ, ptsPAI MANxt + PEP-> MAN1P + PYR Melibiosε melB MELIxt + HEXT-> ELI Enzyme Gene Reaction E. coli genome N-Acetylglucosaminε nagE, ptsN NAG + PEP-> NAGP + PYR 2 Rhamπose rhaT RMNxt + ATP -> RMN + ADP + PI 1 Ribose rbsABCB, xylH RIBxt + ATP-> RIB + ADP + PI 5 Sucrose scr SUCxt + PEP-> SUC6P + PYR 0 Trehalose treAB TRExt + PEP-> TRE6P + PYR 2 Xylose (low affinity) xylE XYLxt + HEXT - > XYL 1 Xylose (high affinity) xylFG, rbsB XYLxt + ATP-> XYL + ADP + PI 3 Alanine cycA ALAxt + ATP-> ALA + ADP + PI 1 Arginine artPMQdl, argThisMQP ARGxt + ATP-> ARG + ADP + PI 9 Asparagine (low Affinity) ASNxt + HEXT <-> ASN 0 Asparagine (high Affinity) ASNxt + ATP-> ASN + ADP + PI 0 Aspartate gltP ASPxt + HEXT-> ASP 1 Aspartate gltdKI ASPxt÷ ATP -> ASP + ADP + PI 3 Branched chain amino acid transport brnO BCAAxt + HEXT <-> BCAA 1 Cysteine not identified CYSxt + ATP-> CYS + ADP + PI 0 D-Alanine cycA DALAxt÷ ATP-> DALA + ADP + PI 1 D-Alanine glycine permease cycA DALAxt + HEXT <-> DALA 1 D-Alanine glycine permease cycA DSERxt + HEXT <-> DSER 1 D-Alanine glycine permease cycA GLYxt + HEXT <-> GLY 1 Diaminopimelic acid MDAPxt÷ ATP-> MDAP + ADP + PI 0 γ-Amiπobutyrate transport gabP GABAxt + ATP-> GABA + ADP + PI 1 Enzyme Gene Reaction E. coli genome Glutamate gltP GLUxt + HEXT <-> GLU 1 Glutamatε gits GLUxt + HEXT <-> GLU 1 Glutamate gltdKL GLUxt + ATP-> GLU + ADP + PI 3 Glutamine glnHPQ GLNxt + ATP-> GLN + ADP + PI 3 Glycinε cycA,proVWX GLYxt + ATP-> GLY + ADP + PI 4 Histidinε hisdMPQ HISxt + ATP-> HIS + ADP + PI 4 Isolεucinε livd ILExt + ATP - > ILE + ADP + PI 1 Leucine livHKM/livFGd LEUxt + ATP-> LEU + ADP + PI 6 Lysiπe lysP LYSxt + HEXT <-> LYS 1 Lysiπe argT, hisMPQ LYSxt + ATP-> LYS + ADP + PI 4 Lysine/Cadavεrinε cadB LYSxt + ATP-> LYS + ADP + PI 1 Mεthioniπe etB METxt + ATP-> MET + ADP + PI 0 Ornithine argT, hisMPQ ORNxt + ATP-> ORN + ADP + PI 4 Phenlyalanine aroP/mtr/pheP PHExt + HEXT <-> PHE 3 Proline putP, proPWX PROxt + HEXT <-> PRO 4 Proline eye A, proVW PR0xt + ATP-> PRO + ADP + PI 4 Putrescinε potEFHIG PTRCxt + ATP-> PTRC + ADP + PI 5 Sεrinε sdaC SERxt + HEXT <-> SER 1 Sεrinε cycA SERxt + ATP-> SER + ADP + PI 1 Spermidine & putrescine potABCB SPMDxt + ATP -> SPMD + ADP + PI 4 Spermidinε & putrescine potABCB PTRCxt + ATP -> PTRC + ADP + PI 4 Enzyme Gene Reaction E. coli genome Threoninε livd THRxt + ATP-> THR + ADP + PI Thrεoninε tdcC THRxt + HEXT <-> THR Tryptophan tnaB TRPxt + HEXT <-> TRP Tyrosine tyrP TYRxt + HEXT <-> TYR Valiπe livd VALxt + ATP -> VAL + ADP + PI Dipeptide dppABCBF DIPEPxt + ATP-> DIPEP + ADP + PI Oligopεptidε oppABCBF OPEPxt + ATP-> 0PEP + ADP + PI Peptide sapABB PEPTxt + ATP-> PEPT + ADP + PI Uracil uraA URAxt + HEXT-> URA Nicotiπamidε moπonuclεotidε transportεr pnuC NMNxt + HEXT-> + NMN Cytosinε codB CYTSxt + HEXT-> CYTS -fc. Adenine purB ADxt + HEXT-> AD Guaniπe gpt, hpt GNxt <-> GN Hypoxanthine gpt, hpt HYXNxt <-> HYXN Xanthosine xapB XTSNxt <-> XTSN Xanthine gpt XANxt <-> XAN Gsystem nupG ADNxt + HEXT-> ADN Gsystem nupG GSNxt + HEXT-> GSN Gsystem nupG URIxt + HEXT-> URI Gsystem nupG CYTDxt + HEXT > CYTD Gsystem (transports all nucleosides) nupG INSxt + HEXT-> INS Enzyme Gene Reaction E. coli genome Gsystem nupG XTSNxt + HEXT-> XTSN Gsystem nupG DTxt + HEXT-> DT Gsystem nupG DINxt + HEXT-> DIN Gsystem nupG DGxt + HEXT-> DG Gsystem nupG DAxt + HEXT-> DA Gsystem nupG DCxt + HEXT-> DC Gsystem nupG DUxt + HEXT-> DU C-system nupC ADNxt + HEXT-> ADN C-system nupC URIxt + HEXT-> URI C-system nupC CYTDxt + HEXT-> CYTD C-system nupC DTxt + HEXT-> DT .p. C-system nupC DAxt + HEXT-> DA σ. C-system nupC DCxt + HEXT-> DC C-system nupC DUxt + HEXT-> DU Nucleosides and deoxynucleoside tsx ADNxt + HEXT-> ADN Nucleosidεs and deoxynucleoside tsx GSNxt + HEXT-> GSN Nucleosides and deoxynucleoside tsx URIxt + HEXT-> URI Nucleosides and deoxynuclεosidε tsx CYTDxt + HEXT-> CYTD Nuclεosidεs and dεoxynuclεoside tsx INSxt + HEXT-> INS Nucleosides and deoxyπucleosidε tsx XTSNxt + HEXT-> XTSN Nuclεosidεs and deoxynucleosidε tsx DTxt + HEXT > DT Enzyme Gene Reaction E. coli genome Nucleosidεs and deoxynuclεosidε tsx DINxt + HEXT - > DIN 1 Nucleosides and deoxynucleoside tsx DGxt + HEXT-> DG 1 Nucleosides and deoxynuclεosidε tsx DAxt + HEXT > DA 1 Nuclεosidεs and dεoxynuclεosidε tsx DCxt + HEXT-> DC 1 Nucleosides and deoxynucleoside tsx DUxt + HEXT-> DU 1 Acetate transport ACxt + HEXT <-> AC 0 Lactate transport LACxt + HEXT <-> LAC 0 L-Lactate lldP LLACxt + HEXT <-> LLAC 1 Formate transport focA FORxt <-> FOR 1 Ethanol transport ETHxt + HEXT <-> ETH 0 Succiπatε transport dcuAB SUCCxt + HEXT <-> SUCC 2 Pyruvate transport PYRxt + HEXT <-> PYR 0 Ammonia transport amtB NH3xt + HEXT <-> NH3 1 Potassium transport kdpABC Kxt + ATP > K + ADP + PI 3 Potassium transport trkAEHG Kxt + HEXT <-> K 3 Sulfate transport cysPTUWAZ, sbp SLFxt + ATP-> SLF + ADP + PI 7 Phosphate transport pstABCS Plxt + ATP-> ADP + 2 PI 4 Phosphate transport pitAB Plxt + HEXT < - > PI 2 Glycerol-3-phosphatε glpT, ugpABCE GL3Pxt + PI-> GL3P 5 Dicarboxγlates dcuAB, dctA SUCCxt÷ HEXT <-> SUCC 3 Dicarboxγlates dcuAB, dctA FUMxt + HEXT <-> FUM 3 Enzyme Gene Reaction E. coli genome Dicarboxylates dcuAB, dctA MALxt + HEXT <-> MAL 3 Dicarboxylates dcuAB, dctA ASPxt + HEXT <-> ASP Fatty acid transport fadL C140xt-> C140 Fatty acid transport fadL C160xt-> C160 Fatty acid transport fadL C180xt-> C180 α-Ketoglutarate kgtP AKGxt + HEXT <-> AKG Na/H antiporter nhaABC NAxt+ <-> NA + HEXT NaJH antiporter chaABC NAxt+ <-> NA + HEXT Pantotheπatε panF PNTOxt + HEXT <-> PNTO Sialic acid permease nanT SLAxt÷ ATP-> SLA + ADP + PI Oxygen transport 02xt <-> 02 Carbon dioxide transport C02xt <-> C02 Urea transport UREAxt+2HEXT <-> UREA ATP drain flux for constant maintaπencε ATP-> ADP + PI requirements Glyceraldehyde transport gufP GLALxt <-> GLAL Acetaldehyde transport ACALxt <-> ACAL Table 2 Comparison of the predicted mutant growth characteristics from the gene deletion study to published experimental results with single and double mutants. Gene Glucose Glyceroi Succinate Acetate (in vivolin silico) (in vivolin silico) (in vivolin silico) (in vivolin silico) aceEF -/+ aceA aceB ackA +/+ acs +/+ acn -/ • -/- -/- cyd +/+ cyo +/+ eno -/+ ■ /+ fba -/+ fbp +/+ • / • ■ I- gap • /- -/- • /- gltA -/- -/- gnd +/+ idh -/ • • /- ndh +/+ +/+ nuo +/+ +/+ pfk -/+ pgi +/+ +/+ pgk • / • • / • pgi +/+ pntAB +/+ +/+ +/+ +/+ glk +/+ ppc ±/+ ■ /+ +/+ +/+ pta +/+ pts +/+ pyk +/+ rpi -/- -/- -/- sdhABCD +/+ tpi • /+ • / • -/- Gene Glucose Glyceroi Succiπate Acetate (in vivolin silico) (in vivolin silico) (in vivolin silico) (in vivolin silico) unc +/+ • / • zwf +/+ sucAD +/+ zwf, pnt +/+ pck, mez pck, pps pgi, zwf pgi, gnd pta,acs tktA, tktB -/ • Results are scored as + or - meaning growth or no growth determined from in vivo I in silico data. In 73 of 80 cases the in silico behavior is the same as the experimentally observed behavior.

Claims: WHAT IS CLAIMED IS: 1. A method for determining the genome specific stoichiometric matrix of an organism, comprising: providing the nucleotide sequence of a metabolic gene in the organism; identifying the open reading frame of the metabolic gene; assigning a function to the metabolic gene based on its nucleotide or amino acid homology to other, known metabolic genes; determining the metabolic genotype of the organism based on the assigned function of the metabolic gene; and determining the genome specific stoichiometric matrix for the organism. 2. The method of Claim 1, further comprising determining a pheπotγpe of the organism. 3. The method of Claim 2, wherein determining the phenotype of the organism comprises analyzing the consequences of reduction or addition to the composition of the metabolic genotype. 4. The method of Claim 2, further comprising identifying lethal genetic deletions. 5. The method of Claim 4 further comprising determining the effectiveness of a drug through analysis of the lethal genetic deletions. 6. The method of Claim 1, further comprising determining the minimal media composition required to sustain growth of the organism. 7. The method of Claim 1, further comprising determining an optimal media composition for growing the organism. 8. The method of Claim 1, further comprising determining the most advantageous complement of genes in the organism necessary to sustain growth in a particular environmental condition. 9. The method of Claim 1 , wherein the organism is Escherichia Coll. 10. The method of Claim 1 , comprising the use of a Flux Based Analysis on the stoichiometric matrix. 11. The method of Claim 1, comprising adding biochemical information for a metabolic gene to the stoichiometric matrix. 12. A method for determining a potential genetic target for a drug that kills an organism, comprising: providing the nucleotide sequence of a metabolic gene in the organism; identifying the open reading frame of the metabolic gene; assigning a function to the metabolic gene based on its nucleotide or amino acid homology to other, known metabolic genes; determining whether the metabolic gene is required for growth of the organism; repeating the providing, identifying, assigning and determining steps for other metabolic genes of the organism; and selecting a gene that is required for growth of the organism as a target for the drug. 13. The method of Claim 12, wherein the organism is Escherichia coli. 14. The method of Claim 12, comprising performing a Flux Based Analysis of a stoichiometric matrix from the organism. 15. The method of Claim 12, comprising the use of biochemical information on the metabolic gene to determine whether it is required for growth of the organism. 16. A computer system comprising a memory having instructions that when executed perform the steps of: providing the nucleotide sequence of a metabolic gene in an organism; identifying the open reading frame of the metabolic gene; assigning a function to the metabolic gene based on its nucleotide or amino acid homology to other, known metabolic genes; determining the metabolic genotype of the organism based on the assigned function of the unknown metabolic gene; and determining the genome specific stoichiometric matrix for the organism. 17. The computer system of Claim 16, wherein said memory is selected from the group consisting of: a hard disk, optical memory, Random Access Memory, Read Only Memory and Flash Memory. 18. The computer system of Claim 16, wherein said computer system is based on an Intel® microprocessor. 19. The computer system of Claim 16, wherein the organism is Escherichia Coli. 20. The computer system of Claim 16, further comprising instructions that when executed perform the method of identifying lethal genetic deletions for the organism. 21. The computer system of Claim 16, comprising instructions, that when executed, add biochemical information on a metabolic gene to the stoichiometric matrix. 22. A method for representing a living organism in a computer system, comprising: providing the nucleotide sequence of a metabolic gene in the organism; identifying the open reading frame of the metabolic gene; assigning a function to the metabolic gene based on its nucleotide or amino acid homology to other, known metabolic genes; determining the metabolic genotype of the organism based on the assigned function of the metabolic gene; determining the genome specific stoichiometric matrix for the organism; and storing the genome specific stoichiometric matrix in a memory of the computer. 23. The method of Claim 22, wherein the organism is Escherichia coli. 24. The method of Claim 22, comprising the use of Flux Based Analysis to analyze the stoichiometric matrix. 25. The method of Claim 22, comprising adding biochemical information on a metabolic gene to determine the metabolic genotype of the organism. 26. The method of Claim 22, comprising calculating the genome specific stoichiometric matrix using Flux Based Analysis. 27. A genome specific stoichiometric matrix representing the metabolism of a living organism, produced by a process comprising: providing the nucleotide sequence of a metabolic gene in the organism; identifying the open reading frame of the metabolic gene; assigning a function to the metabolic gene based on its nucleotide or amino acid homology to other, known metabolic genes; determining the metabolic genotype of the organism based on the assigned function of the metabolic gene; and determining the genome specific stoichiometric matrix for the organism. 28. The stoichiometric matrix of Claim 1 , wherein the organism is Escherichia Coli. 29. The stoichiometric matrix of Claim 1, wherein the stoichiometric matrix is determined using Flux Based Analysis. 30. The stoichiometric matrix of Claim 1, produced by the process of adding biochemical information for the metabolic gene.

Inventors: PALSSON BERNHARD, PALSSON, BERNHARD
Applicants: PALSSON BERNHARD, PALSSON, BERNHARD
Application year: 2000
Main classification: C12Q1
IPC codes: C12N15, G06F19