Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets
© Singh and Ghosh; licensee BioMed Central Ltd. 2006
Received: 03 April 2006
Accepted: 03 August 2006
Published: 03 August 2006
Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model.
We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two iso citrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of iso citrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both iso citrate dehydrogenase 1 and iso citrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active iso citrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates iso citrate dehydrogenase 1 and/or the proposed inactivator of iso citrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of iso citrate lyases along with a reduction in the inactivation of iso citrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria.
We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.
Tuberculosis is an ancient disease that has plagued humans for centuries, and presently there is an urgent need for new drugs to combat drug-resistant tuberculosis and shorten the time of tuberculosis therapy. Tuberculosis treatment is lengthy because of a population of persistent bacilli that is not effectively eliminated by current drugs.
The persistent bacilli primarily use fatty acids as their carbon source . This makes the glyoxylate bypass, consisting of iso citrate lyase (ICL) and malate synthase (MS), essential for the bacterium; in its absence there will be no net formation of the intermediates required for synthesizing cellular materials. Inhibition of both ICL1 (prokaryotic-like isoform) and ICL2 (eukaryotic-like isoform) has been shown to block the growth of M. tuberculosis in macrophages and in mice . Hence, interference with the glyoxylate bypass is a potential approach to the design of new drugs against persistent mycobacteria. This is consistent with the suggestion that the regulation of M. tuberculosis metabolism in response to the environment of the bacterium makes large contributions to its virulence .
At the branch point of the tricarboxylic acid (TCA) cycle and glyoxylate bypass, iso citrate dehydrogenase (ICD), involved in the TCA cycle, and ICL, involved in the glyoxylate bypass, compete for the same substrate, namely iso citrate (ICIT). In Escherichia coli, flux at this branch point is predominantly controlled through the reversible inactivation of ICD by phosphorylation, catalyzed by ICD-kinase . We have already identified the kinase in M. tuberculosis, equivalent to ICD-kinase in E. coli, that is responsible for reversible inactivation of ICD1 (Rv3339c) by phosphorylation . Moreover, a method has been described for inhibiting a metabolic pathway that is essential for the viability of a microorganism by diverting the substrate to a different metabolic pathway, and it has been suggested that inhibiting ICD1-kinase could inhibit the flux through the glyoxylate bypass in M. tuberculosis . Since inhibition of ICD1-kinase would increase the amount of dephosphorylated (active) ICD1, the flux through the glyoxylate bypass would be diminished. However, enzymes are not isolated entities in living organisms but act as components of systems, so the effect of modulation of any enzyme activity on a metabolic flux depends on the properties of the other enzymes in the pathway concerned .
Metabolic Control Analysis (MCA) is a theoretical framework that relates the systemic properties of a metabolic system to the properties of its components, in particular the enzymes, in a quantitative manner . Application of MCA to the identification of potential drug targets is exemplified by glycolysis in Trypanosoma brucei [7–9]. MCA also gives insight into the cellular effect(s) of inhibition of a particular enzyme. Eisenthal et al.  suggested two basic metabolic methods for killing an organism: decreasing the flux through an essential metabolic pathway to a nonviable level, or increasing the concentration of a metabolite to a toxic level. Therefore, if inhibition of an enzyme kills an organism, MCA can elucidate the mechanism involved.
Since modulation of target enzyme(s) activity is usually aimed at altering the cell's metabolic profile, knowledge of the metabolic profile is important for identifying the target. Recent experiments have shown a positive correlation between mRNA levels measured by DNA microarrays and protein abundance in both E. coli  and yeast cells [11, 12], so the gene expression profile could be connected to the metabolic profile via simulation of the pathway under study. In E. coli, the in vivo kinetic parameters required for estimating the metabolic profile of most enzymes are available when the organism is grown using glucose as the carbon source . In contrast, when acetate is used as the carbon source, the gene expression profile of the TCA cycle and glyoxylate bypass enzymes differed from that found with glucose . The corresponding metabolic flux distributions in central metabolic pathways under both growth conditions are known , so this seems an ideal system for testing the hypothesis that the gene expression profile can be connected with the metabolic profile via simulation of the pathway under study.
In this communication, we describe the construction of a kinetic model of the TCA cycle and glyoxylate bypass in M. tuberculosis, and we study the likely metabolic consequences of inhibiting ICLs and ICD1-kinase. To the best of our knowledge, this is the first attempt to model any specific metabolic pathway in M. tuberculosis, and no kinetic model is available for the TCA cycle and glyoxylate bypass in this bacterium. Initially, we constructed a kinetic model for the TCA cycle and glyoxylate bypass in E. coli to validate the pathway modeling protocol used and to test how well the metabolic profile correlates with the gene expression profile while trying to predict the metabolic flux distribution using the gene expression data.
Metabolites of the models with known concentrations (with references indicated in square brackets)
Concentration in glucose condition (in mM)
Concentration in acetate condition (in mM)
Concentration (in mM)
2.464 (derived from Tian et. al )
0.08528 (derived from Tian et. al )
0.408 (derived from Tian et. al )
Results and discussion
Steady state solution for the models
Steady state fluxes computed for E. coli model.
Growth on glucose (mM/min)
Growth on acetate (mM/min)
Comparison of the experimental fluxes to that computed from E. coli model. The reaction step SYN was not explicitly mentioned by Zhao et al. , but was shown by a branch from αKG.
Growth on glucose (Experimental)
Growth on glucose (Simulation)
Growth on acetate (Experimental)
Growth on acetate (Simulation)
Since the maximal reaction rates (Vmax) of the enzymes during growth on acetate were estimated using gene expression data, it is possible to estimate the changes in metabolic flux distribution due to changes in gene expression via simulation of the biochemical pathway under study. This was also noted in the study of branched chain amino acid biosynthesis in E. coli .
Steady state fluxes computed for M. tuberculosis model-1 and model-2 (in persistent mycobacteria)
Fluxes in model-1 (mM/min)
Fluxes in model-2 (mM/min)
-5.65892 × 10-11
On the basis of the finding of Tian et al. , i.e. that KDH activity is absent in M. tuberculosis, and of the observation that there is little difference between the two models in the turnover of the TCA cycle and glyoxylate bypass, M. tuberculosis model-2 was taken as the reference model in the remaining parts of this study.
Inactivation of ICDs in M. tuberculosis model
Deletion of genes encoding ICLs in M. tuberculosis model
McKinney and co-workers showed that deletion of either of the genes icl1 or icl2 had little effect on mycobacterial growth in macrophages or in mice . In our model, deletion of icl1 could be simulated by deleting the ICL1 reaction. Plots of JICD1 + JICD2 and JICL2 as a function of VfICD1 and VfICD2 (figure 2C) showed that more than 90% inactivation of both ICD1 and ICD2 is required to allow a perceptible flux through the glyoxylate bypass in the absence of ICL1. In contrast, when both ICLs were present, 70% inactivation of both ICD1 and ICD2 sufficed to allow a flux through the glyoxylate bypass (figure 2B). Simulating icl2 gene deletion showed only a marginal difference in the flux through the glyoxylate bypass or in JICD1 + JICD2 when plotted against VfICD1 and VfICD2 (figure 2D), compared to the fluxes observed in the presence of both ICLs (figure 2B). Thus, the model correctly simulates the experimental observation that deletion of either of the two ICL genes has little effect on the growth of mycobacteria in macrophages and in mice . It also shows that a flux of approximately 26% through the glyoxylate bypass remains in the absence of icl1, compared to the flux when both ICLs are present (with VfICD1 and VfICD2 kept at 5% of the original values). In the absence of icl2, the flux through the glyoxylate bypass decreases only by 7.6% compared to the flux in presence of both ICLs (with VfICD1 and VfICD2 kept at 5% of the original values). Such a reduction in flux due to the deletion of either of the two ICL genes would be too small to lead to elimination of the bacilli.
Competitive inhibition of ICLs
An increase was observed in the efficiency of competitive inhibition of ICL1 and ICL2 with an increase in VfICD1 and VfICD2 from 2.5% to 5% of the original values, because at lower VfICD1 and VfICD2, inhibition of ICL1 and ICL2 leads to an increase in iso citrate concentration, nullifying the effect of competitive inhibition.
Uncompetitive inhibition of ICLs
In contrast to competitive inhibition of ICL1 and ICL2, the efficiency of uncompetitive inhibition decreased with an increase in VfICD1 and VfICD2 from 2.5% to 5% of the original values. This is because an increase in the Vmax of the ICDs leads to a decrease in iso citrate concentration, and hence to a decrease in the enzyme-substrate complex concentration. Because an uncompetitive inhibitor binds only to the enzyme-substrate complex, a decrease in enzyme-substrate complex concentration leads to a decrease in inhibitor binding, resulting in less inhibition.
The increase in efficiency of competitive inhibition with an increase in the Vmax of the ICDs leads to an alternative strategy for killing mycobacteria, i.e. by using a competitive inhibitor of ICL1 and ICL2 along with inhibition of ICD1-kinase and/or the proposed inactivator of ICD2. Inhibition of ICD1-kinase and/or proposed inactivator of ICD2 would increase the amount of active ICD1 and/or ICD2, i.e. would indirectly cause an increase in the Vmax of ICD1 and/or ICD2, thus indirectly improving the efficiency of competitive inhibition of the ICLs by the available iso citrate and reducing the competition between the substrate iso citrate and inhibitor. The points to note in this strategy are: (i) a competitive inhibitor of ICLs can serve the purpose; and (ii) the percentage inhibition of the ICD-kinase and/or proposed inactivator of ICD2 required here would be less than required to increase the amount of active ICD1 and/or ICD2 sufficiently to stop the flux through the glyoxylate bypass.
Mixed inhibition of ICLs
Considering that we focused on the TCA cycle and glyoxylate bypass only, and that the model was built with a number of permissible assumptions, the results obtained agree satisfactorily with the experimental data. The observation that inhibition of ICLs results in no marked changes in the concentrations of any other metabolites in the model (result not shown), but to a decrease in the flux through glyoxylate bypass, indicates that the clearing of mycobacterial load from macrophages as observed by McKinney and co-workers  can be correlated with a decrease in the glyoxylate bypass flux, not with accumulation of any toxic metabolite.
This study constitutes a proof of concept: one can use kinetic modeling of biochemical pathways to investigate potential drug targets and to infer the type of inhibition appropriate for eliminating the pathogen. The study highlights the difference between the inhibitor concentrations required in vitro and in vivo to inhibit the glyoxylate bypass pathway enzymes. The advantage of this approach to assessing drug targets is that it facilitates the study of systemic effect(s) of modulating the target enzyme(s) on the pathway. The applicability of the study is certainly limited by the approximations and assumptions made while constructing the models, but these should be overcome soon because the required data are accumulating rapidly in this post-genomic era.
The steps in the construction of the kinetic model are described below.
Biochemical reactions in the pathway
The biochemical reactions of the E. coli TCA cycle and glyoxylate bypass were obtained from EcoCyc , and those of M. tuberculosis from MetaCyc . These reactions for the two organisms from the two different data sources were identical. A reaction branching from α-ketoglutarate (αKG = precursor; named SYN in the models) was added to both the E. coli and M. tuberculosis models to account for the fraction of αKG utilized for precursor biosynthesis (as shown by Zhao et al.  in E. coli). A set of two reactions catalyzed by α-ketoglutarate decarboxylase (KGD) and succinic semialdehyde dehydrogenase (SSADH) that together convert αKG to succinate (SUC) via succinic semialdehyde (SSA) was also included in the M. tuberculosis model. The model also accounted for the presence of two isoforms of ICD , ICD1 (Rv3339c) and ICD2 (Rv0066c), and two isoforms of ICL [17, 18], ICL1 (Rv0467) and ICL2 (Rv1915 and Rv1916), in M. tuberculosis H37Rv strain. The requisite co-enzymes and co-factors were assumed to be present in large excess so their effects on the reaction rates in the models were ignored. The reactions considered in the construction of the models are shown in figure 1.
Recently, Nathan and co-workers failed to detect α-ketoglutarate dehydrogenase (KDH) activity in M. tuberculosis . They suggested that Rv1248c, annotated as encoding SucA, the putative E1 component of KDH, encodes KGD and produces SSA. SSA is then converted by SSADH to SUC. This new finding was also incorporated into our study by constructing another model for M. tuberculosis (named M. tuberculosis model-2) in which the KDH reaction was removed (see figure 1).
Michaelis-Menten equations for one substrate and two-substrate reactions were used to describe the reaction kinetics in the models. The reversible Michaelis-Menten equation for two non-competing product-substrate couples is shown in equation (4) :
where v = net rate of the reaction; Vf, Vr = maximal rates of the forward and reverse reaction, respectively; S1, S2 = concentrations of substrates S1 and S2 respectively; P1, P2 = concentrations of products P1 and P2 respectively; KS1, KS2, KP1, KP2 = Michaelis-Menten constants for S1, S2, P1 and P2 respectively.
The only reaction in which a different kinetic equation was used was the reaction: ICIT = SUC + glyoxylate (GLY), catalyzed by ICL. This is known to occur by an ordered uni-bi mechanism  as described by Bakker et. al .
Parameters of the models
The kinetic parameters of the enzymes in the models (see [additional file 1: Kinetic constants of the enzymes in E. coli model'] and [additional file 2: Kinetic constants of the enzymes in M. tuberculosis model-1 and model-2]) were either obtained from publicly available databases, namely CyberCell Database (CCDB)  and BRENDA , or extracted from the literature. The maximal reaction rates (Vmax) expressed in nmol/min/mg protein were converted to mM/min by taking the intracellular volume of a bacterial cell as 2 × 10-12 ml  and the total protein content as 3.2 × 10-10 mg . We were interested in studying the reactions of the pathway in the catabolic direction, i.e. the direction in which it usually works in the cell; so in cases where the value of Vr was not available it was taken as a fraction of Vf (after some trial and error, Vr = Vf/100). In cases where reverse reaction had been monitored and Vr reported, Vf was taken as equal to Vr. Where a KM was not available, usually for a reverse reaction, it was assumed to be equal to 10 × KM of the substrate from which that product was formed (by the same logic as used for the Vr values). The metabolites acetyl-CoA, oxaloacetate and CoA were considered as boundary metabolites, so their concentrations were fixed in the simulations. The initial concentration of each variable metabolite was taken as 2 × KM for the reaction for which that metabolite is a substrate (except for those metabolites of which the concentrations were known; see table 1).
In the E. coli model, the carbon flux through the pathway was predicted under two growth conditions, viz. growth on glucose and acetate as carbon sources. Most enzyme kinetic parameters are available for E. coli grown on glucose, but it is also necessary to estimate the enzyme kinetic parameters for the acetate condition. The changes in E. coli gene expression when growth shifts from glucose to acetate were described by Oh et al. . Assuming that the change in mRNA level leads to a proportional change in protein level (enzyme level in our study), there would be a proportional change in the Vmax of that enzyme (because Vmax is proportional to the amount of enzyme). Thus, using the Vmax values of enzymes under the glucose condition and the fold change in gene expression of the corresponding enzymes, the Vmax values under the acetate condition were calculated.
Calculation of Vmax from gene expression data
Let, the expression levels of a gene g1 under the acetate and glucose conditions be g1a and g1g respectively. Therefore, the fold change when growth shifts from glucose to acetate is n = g1a/g1g. Taking account of the assumption that a change in mRNA level leads to a proportional change in protein level,
p1a/p1g = g1a/g1g = n equation 5
where p1 is the amount of the protein encoded by g1 and the subscripts 'a' and 'g' denote its level in acetate and glucose respectively
Since Vmax = kcat × E (where kcat = turnover number, E = amount of enzyme catalyzing the reaction) and kcat is a constant, Vmax α E
Therefore, from equation 5, Vmaxa/Vmaxg = n
(where Vmaxa, Vmaxg = Vmax of the enzyme in acetate and glucose respectively)
or Vmaxa = n × Vmaxg
Thus, using the values of n and Vmaxg, Vmaxa values were calculated and used as parameters for the model to simulate the condition of growth on acetate as the carbon source.
The rate of the SYN reaction was maintained at 0.188 times (for glucose condition) and 0.0341 times (for acetate condition) the rate of the ICD reaction in the E. coli model, as shown experimentally . Owing to the unavailability of data for M. tuberculosis, the rate of the SYN reaction was maintained at that under acetate conditions in E. coli. The kinetic parameters for M. tuberculosis KDH were also assumed to be same as for E. coli. As ICL activity in persistent mycobacteria is 4 times that in the normal condition , the concentration of the ICLs were taken as 4 times those in normal conditions.
Simulations were performed by writing scripts for Jarnac 2.14 . First, steady states were calculated, then – starting from the steady state solution for each model – a time-dependent simulation was performed to test the stability of the steady state. We checked that the program Gepasi 3.30  generates the same results as Jarnac given the same input, but we continued our work with Jarnac because it offered us the flexibility of writing our own scripts.
The fluxes computed from the models were expressed in mM/min. To compare the steady state fluxes of the E. coli model with experimental findings , they were converted to the units in which experimental fluxes were expressed. The experimental fluxes were expressed relative to (a) molar glucose uptake or (b) molar acetate uptake rate depending on the carbon source. The following steps were used to convert the units: flux through citrate synthase during growth on glucose = 50; flux through citrate synthase during growth on glucose in the model = 4.187 mM/min; hence, conversion factor x = (50)/(4.187 mM/min). Using this conversion factor (x), all the fluxes computed from the model were converted to the units in which experimental fluxes were expressed.
Example: flux through α-ketoglutarate dehydrogenase (KDH) reaction step in the model = 3.394 mM/min = (3.394 mM/min) × (x min/mM) = 40.5.
A similar conversion factor was calculated for growth on acetate using flux through the citrate synthase step.
iso citrate lyase
iso citrate dehydrogenase
flux through ICD1
flux through ICD2
flux through ICL1
flux through ICL2
inhibitor constant of inhibitor I
Metabolic Control Analysis
succinic semialdehyde dehydrogenase
maximal rate of the forward reaction
Vmax of the reaction catalyzed by ICD1 in the forward direction
Vmax of the reaction catalyzed by ICD2 in the forward direction
maximal rate of an enzymatic reaction
maximal rate of the reverse reaction.
We thank Dr. S. Datta, AstraZeneca R&D, Bangalore, and Dr. S. Sinha, Centre for Cellular and Molecular Biology, Hyderabad, for interesting discussions and help provided during this work. We are also grateful to Dr. V. Shankar, Institute of Bioinformatics and Biotechnology, University of Pune, Pune for his inputs on the presentation and flow of the manuscript. V. K. Singh would like to thank Department of Biotechnology, Government of India, for providing the Junior Research Fellowship. We are thankful to the referees for painstakingly reading the manuscript and giving valuable suggestions.
- Bishai W: Lipid lunch for persistent pathogen. Nature. 2000, 406: 683-685. 10.1038/35021159.View ArticlePubMedGoogle Scholar
- Muñoz-Elías EJ, McKinney JD: Mycobacterium tuberculosis isocitrate lyases 1 and 2 are jointly required for in vivo growth and virulence. Nat Med. 2005, 11: 638-644. 10.1038/nm1252.PubMed CentralView ArticlePubMedGoogle Scholar
- McAdam RA, Quan S, Smith DA, Bardarov S, Betts JC, Cook FC, Hooker EU, Lewis AP, Woollard P, Everett MJ, Lukey PT, Bancroft GJ, Jacobs WR, Duncan K: Characterization of a Mycobacterium tuberculosis H37Rv transposon library reveals insertions in 351 ORFs and mutants with altered virulence. Microbiology. 2002, 148: 2975-2986.View ArticlePubMedGoogle Scholar
- LaPorte DC, Walsh K, Koshland DE: The branch point effect ultrasensitivity and subsensitivity to metabolic control. J Biol Chem. 1984, 259: 14068-14075.PubMedGoogle Scholar
- Balganesh TS, Datta S, Ghosh I: WO 2004/087943 A1. 2004Google Scholar
- Fell DA: Metabolic Control Analysis: a survey of its theoretical and experimental development. Biochem J. 1992, 286: 313-330.PubMed CentralView ArticlePubMedGoogle Scholar
- Bakker BM, Michels PAM, Opperdoes FR, Westerhoff HV: Glycolysis in bloodstream form Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes. J Biol Chem. 1997, 272: 3207-3215. 10.1074/jbc.272.6.3207.View ArticlePubMedGoogle Scholar
- Bakker BM, Michels PAM, Opperdoes FR, Westerhoff HV: What controls glycolysis in bloodstream form Trypanosoma brucei?. J Biol Chem. 1999, 274: 14551-14559. 10.1074/jbc.274.21.14551.View ArticlePubMedGoogle Scholar
- Eisenthal R, Cornish-Bowden A: Prospects for antiparasitic drugs the case of Trypanosoma brucei, the causative agent of African sleeping sickness. J Biol Chem. 1998, 273: 5500-5505. 10.1074/jbc.273.10.5500.View ArticlePubMedGoogle Scholar
- Arfin SM, Long AD, Ito ET, Tolleri L, Riehle MM, Paegle ES, Hatfield GW: Global gene expression profiling in Escherichia coli K12 the effects of integration host factor. J Biol Chem. 2000, 275: 29672-29684. 10.1074/jbc.M002247200.View ArticlePubMedGoogle Scholar
- Futcher B, Latter GI, Monardo P, McLaughlin CS, Garrels JI: A sampling of the yeast proteome. Mol Cell Biol. 1999, 19: 7357-7368.PubMed CentralPubMedGoogle Scholar
- Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001, 292: 929-934. 10.1126/science.292.5518.929.View ArticlePubMedGoogle Scholar
- Tian J, Bryk R, Itoh M, Suematsu M, Nathan C: Variant tricarboxylic acid cycle in Mycobacterium tuberculosis: Identification of α-ketoglutarate decarboxylase. Proc Natl Acad Sci USA. 2005, 102: 10670-10675. 10.1073/pnas.0501605102.PubMed CentralView ArticlePubMedGoogle Scholar
- Oh MK, Rohlin L, Kao KC, Liao JC: Global expression profiling of acetate-grown Escherichia coli. J Biol Chem. 2002, 277: 13175-13183. 10.1074/jbc.M110809200.View ArticlePubMedGoogle Scholar
- Zhao J, Shimizu K: Metabolic flux analysis of Escherichia coli K12 grown on 13C-labeled acetate and glucose using GC-MS and powerful flux calculation method. J Biotechnol. 2003, 101: 101-117. 10.1016/S0168-1656(02)00316-4.View ArticlePubMedGoogle Scholar
- Walsh K, Koshland DE: Branch point control by the phosphorylation state of isocitrate dehydrogenase a quantitative examination of fluxes during a regulatory transition. J Biol Chem. 1985, 260: 8430-8437.PubMedGoogle Scholar
- Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE, Tekaia F, Badcock K, Basham D, Brown D, Chillingworth T, Connor R, Davies R, Devlin K, Feltwell T, Gentles S, Hamlin N, Holroyd S, Hornsby T, Jagels K, Krogh A, Mclean J, Moule S, Murphy L, Oliver K, Osborne J, Quail MA, Rajandream M-A, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG: Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature. 1998, 393: 537-544. 10.1038/31159.View ArticlePubMedGoogle Scholar
- Höner zu Bentrup K, Miczak A, Swenson DL, Russell DG: Characterization of activity and expression of isocitrate lyase in Mycobacterium avium and Mycobacterium tuberculosis. J Bacteriol. 1999, 181: 7161-7167.PubMed CentralPubMedGoogle Scholar
- Yang CR, Shapiro BE, Hung SP, Mjolsness ED, Hatfield GW: A mathematical model for the branched chain amino acid biosynthetic pathways of Escherichia coli K12. J Biol Chem. 2005, 280: 11224-11232. 10.1074/jbc.M411471200.View ArticlePubMedGoogle Scholar
- Schloss JV, Cleland WW: Inhibition of isocitrate lyase by 3-nitropropionate, a reaction-intermediate analogue. Biochemistry. 1982, 21: 4420-4427. 10.1021/bi00261a035.View ArticlePubMedGoogle Scholar
- Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S, Paulsen IT, Peralta-Gil M, Karp PD: EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res. 2005, 33: D334-D337. 10.1093/nar/gki108.PubMed CentralView ArticlePubMedGoogle Scholar
- Krieger CJ, Zhang P, Mueller LA, Wang A, Paley S, Arnaud M, Pick J, Rhee SY, Karp PD: MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 2004, 32: D438-D442. 10.1093/nar/gkh100.PubMed CentralView ArticlePubMedGoogle Scholar
- Segel IH: Enzyme Kinetics: Behavior and analysis of rapid equilibrium and steady-state enzyme systems. 1993, New York: Wiley Classics Library Edition, John Wiley & Sons, IncGoogle Scholar
- Reinscheid DJ, Eikmanns BJ, Sahm H: Characterization of the isocitrate lyase gene from Cornebacterium glutamicum and biochemical analysis of the enzyme. J Bacteriol. 1994, 176: 3474-3483.PubMed CentralPubMedGoogle Scholar
- Sundararaj S, Guo A, Habibi-Nazhad B, Rouani M, Stothard P, Ellison M, Wishart DS: The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli. Nucleic Acids Res. 2004, 32: D293-D295. 10.1093/nar/gkh108.PubMed CentralView ArticlePubMedGoogle Scholar
- Schomburg I, Chang A, Schomburg D: BRENDA, enzyme data and metabolic information. Nucleic Acids Res. 2002, 30: 47-49. 10.1093/nar/30.1.47.PubMed CentralView ArticlePubMedGoogle Scholar
- Mengin-Lecreulx D, Flouret B, van Heijenoort J: Cytoplasmic steps of peptidoglycan synthesis in Escherichia coli. J Bacteriol. 1982, 151: 1109-1117.PubMed CentralPubMedGoogle Scholar
- Wayne LG, Lin KY: Glyoxylate metabolism and adaptation of Mycobacterium tuberculosis to survival under anaerobic conditions. Infect Immun. 1982, 37: 1042-1049.PubMed CentralPubMedGoogle Scholar
- Sauro HM, Hucka M, Finney A, Wellock C, Bolouri H, Doyle J, Kitano H: Next generation simulation tools: The Systems Biology Workbench and BioSPICE integration. OMICS. 2003, 7: 355-372. 10.1089/153623103322637670.View ArticlePubMedGoogle Scholar
- Mendes P, Kell DB: Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics. 1998, 14: 869-883. 10.1093/bioinformatics/14.10.869.View ArticlePubMedGoogle Scholar
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