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0142025925
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edn September 1, 1998. Edited by: The Institute for Genomic Research
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TIGR: Microbial database. edn September 1, 1998. Edited by: The Institute for Genomic Research; 1998.
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TIGR: Microbial Database
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2
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0037433717
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Evaluation of annotation strategies using an entire genome sequence
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Iliopoulos I., Tskoka S., Andrade M.A., Enright A.J., Carroll M., Poullet P., Promponas V., Liakopoulos T., Palaios G., Pasquier C.et al. Evaluation of annotation strategies using an entire genome sequence. Bioinformatics. 19:2003;717-726.
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Iliopoulos, I.1
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Promponas, V.7
Liakopoulos, T.8
Palaios, G.9
Pasquier, C.10
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4
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0034625143
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The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
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Edwards J.S., Palsson B.O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl. Acad Sci. USA. 97:2000;5528-5533.
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Proc. Natl. Acad Sci. USA
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Edwards, J.S.1
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5
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Systems properties of the Haemophilus influenzae Rd metabolic genotype
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Edwards J.S., Palsson B.O. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274:1999;17410-17416.
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Edwards, J.S.1
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0037313750
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Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network
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The largest FBA model to date, consisting of 1175 metabolic reactions and 584 metabolites
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Forster J., Famili I., Fu P., Palsson B.O., Nielsen J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13:2003;244-253 The largest FBA model to date, consisting of 1175 metabolic reactions and 584 metabolites.
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Genome Res.
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Forster, J.1
Famili, I.2
Fu, P.3
Palsson, B.O.4
Nielsen, J.5
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8
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0027909387
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Biochemical production capabilities of Escherichia coli
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Varma A., Boesch B.W., Palsson B.O. Biochemical production capabilities of Escherichia coli. Biotechnol. Bioeng. 42:1993;59-73.
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Varma, A.1
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Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors
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Varma A., Palsson B.O. Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors. J. Theor. Biol. 165:1993;477-502.
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Varma, A.1
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Metabolic capabilities of Escherichia coli: II. Optimal growth patterns
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Varma A., Palsson B.O. Metabolic capabilities of Escherichia coli: II. Optimal growth patterns. J. Theor. Biol. 165:1993;503-522.
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Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110
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Varma A., Palsson B.O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60:1994;3724-3731.
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0027223982
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Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates
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Varma A., Boesch B.W., Palsson B.O. Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl. Environ. Microbiol. 59:1993;2465-2473.
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Varma, A.1
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13
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Analysis of optimality in natural and perturbed metabolic networks
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Defined minimization of metabolic adjustment to more accurately predict knockouts that would be lethal to cellular systems
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Segre D., Vitkup D., Church G.M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad Sci. USA. 99:2002;15112-15117 Defined minimization of metabolic adjustment to more accurately predict knockouts that would be lethal to cellular systems.
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Segre, D.1
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Church, G.M.3
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0034447725
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Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems
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Schilling C.H., Edwards J.S., Letscher D., Palsson B.O. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems. Biotechnol. Bioeng. 71:2000;286-306.
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Schilling, C.H.1
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Letscher, D.3
Palsson, B.O.4
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15
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0036330798
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Genome-scale metabolic model of Helicobacter pylori 26695
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Schilling C.H., Covert M.W., Famili I., Church G.M., Edwards J.S., Palsson B.O. Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol. 184:2002;4582-4593.
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J. Bacteriol.
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Schilling, C.H.1
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Famili, I.3
Church, G.M.4
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Palsson, B.O.6
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16
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0033136116
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Toward metabolic phenomics: Analysis of genomic data using flux balances
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Schilling C.H., Edwards J.S., Palsson B.O. Toward metabolic phenomics: analysis of genomic data using flux balances. Biotechnol. Prog. 15:1999;288-295.
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Schilling, C.H.1
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Palsson, B.O.3
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18
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In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data
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Edwards J.S., Ibarra R.U., Palsson B.O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19:2001;125-130.
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Edwards, J.S.1
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Palsson, B.O.3
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19
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0037079023
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Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth
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This work demonstrated that, while the path of evolution cannot be predicted, the final endpoint of evolution for E. coli can be predicted using FBA
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Ibarra R.U., Edwards J.S., Palsson B.O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature. 420:2002;186-189 This work demonstrated that, while the path of evolution cannot be predicted, the final endpoint of evolution for E. coli can be predicted using FBA.
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Nature
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Ibarra, R.U.1
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Palsson, B.O.3
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20
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0034661295
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Recursive MILP model for finding all alternate optima in LP models for metabolic networks
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Lee S., Phalakornkule C., Domach M.M., Grossmann I.E. Recursive MILP model for finding all alternate optima in LP models for metabolic networks. Comp. Chem. Eng. 24:2000;711-716.
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Lee, S.1
Phalakornkule, C.2
Domach, M.M.3
Grossmann, I.E.4
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21
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0034805665
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A MILP-based flux alternative generation and NMR experimental design strategy for metabolic engineering
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Phalakornkule C., Lee S., Zhu T., Koepsel R., Ataai M.M., Grossmann I.E., Domach M.M. A MILP-based flux alternative generation and NMR experimental design strategy for metabolic engineering. Metab. Eng. 3:2001;124-137.
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Phalakornkule, C.1
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Ataai, M.M.5
Grossmann, I.E.6
Domach, M.M.7
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22
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0036350955
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The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy
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Papin J.A., Price N.D., Edwards J.S., Palsson B.O. The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy. J. Theor. Biol. 215:2002;67-82.
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23
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Regulation of gene expression in flux balance models of metabolism
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Covert M.W., Schilling C.H., Palsson B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213:2001;73-88.
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Covert M.W., Palsson B.O. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J. Theor. Biol. 221:2003;309-325.
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Covert, M.W.1
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25
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0036708443
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Dynamic flux balance analysis of diauxic growth in Escherichia coli
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Combined, these two manuscripts provide approaches for capturing the dynamic shifts that occur in gene regulation over time. Although substantially different, the two approaches compliment each other.
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Mahadevan R., Edwards J.S., Doyle F.J. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys. J. 83:2002;1331-1340 Combined, these two manuscripts provide approaches for capturing the dynamic shifts that occur in gene regulation over time. Although substantially different, the two approaches compliment each other.
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Mahadevan, R.1
Edwards, J.S.2
Doyle, F.J.3
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26
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0036286631
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Energy balance for analysis of complex metabolic networks
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Concentration-dependent chemical potentials are used to specify additional nonlinear constraints on the FBA optimization problem. The approach also allows for determination of intracellular concentrations; however, there is no guarantee that the solution is the global optimum.
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Beard D.A., Liang S.C., Qian H. Energy balance for analysis of complex metabolic networks. Biophys. J. 83:2002;79-86 Concentration-dependent chemical potentials are used to specify additional nonlinear constraints on the FBA optimization problem. The approach also allows for determination of intracellular concentrations; however, there is no guarantee that the solution is the global optimum.
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Biophys. J.
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Liang, S.C.2
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27
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0035812464
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Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions
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Burgard A.P., Maranas C.D. Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. Biotechnol. Bioeng. 74:2001;364-375.
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0037021804
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Characterizing the metabolic phenotype: A phenotype phase plane analysis
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Edwards J.S., Ramakrishna R., Palsson B.O. Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol. Bioeng. 77:2002;27-36.
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Palsson, B.O.3
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29
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0037023907
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Stoichiometric model for evaluating the metabolic capabilities of the facultative methylotroph Methylobacterium extorquens AM1, with application to reconstruction of C-3 and C-4 metabolism
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Computational prediction and experimental validation of network redundancy.
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Van Dien S.J., Lidstrom M.E. Stoichiometric model for evaluating the metabolic capabilities of the facultative methylotroph Methylobacterium extorquens AM1, with application to reconstruction of C-3 and C-4 metabolism. Biotechnol. Bioeng. 78:2002;296-312 Computational prediction and experimental validation of network redundancy.
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Transcriptional regulation in constraints-based metabolic models of Escherichia coli
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Covert M.W., Palsson B.O. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J. Biol. Chem. 277:2002;28058-28064.
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Robustness analysis of the Escherichia coli metabolic network
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Edwards J.S., Palsson B.O. Robustness analysis of the Escherichia coli metabolic network. Biotechnol. Prog. 16:2000;927-939.
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0038293216
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Optimization-based framework for inferring and testing hypothesized metabolic objective functions
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Optimization is used to determine if a given objective function is consistent with experimentally observed fluxes in E. coli.
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Burgard A.P., Maranas C.D. Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol. Bioeng. 82:2003;670-677 Optimization is used to determine if a given objective function is consistent with experimentally observed fluxes in E. coli.
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Calculating as many fluxes as possible in underdetermined metabolic networks
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Although it is not possible to uniquely determine all of the fluxes in an underdetermined system, some of the fluxes can be uniquely specified.
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Klamt S., Schuster S. Calculating as many fluxes as possible in underdetermined metabolic networks. Mol. Biol. Rep. 29:2002;243-248 Although it is not possible to uniquely determine all of the fluxes in an underdetermined system, some of the fluxes can be uniquely specified.
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Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria
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Klamt S., Schuster S., Gilles E.D. Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnol. Bioeng. 77:2002;734-751.
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Equations and calculations for fermentations of butyric acid bacteria
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Fat synthesis in adipose tissue. An examination of stoichiometric constraints
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Savinell J.M., Palsson B.O. Optimal selection of metabolic fluxes for in vivo measurement. I. Development of mathematical methods. J. Theor. Biol. 155:1992;201-214.
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Optimal selection of metabolic fluxes for in vivo measurement. II. Application to Escherichia coli and hybridoma cell metabolism
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Savinell J.M., Palsson B.O. Optimal selection of metabolic fluxes for in vivo measurement. II. Application to Escherichia coli and hybridoma cell metabolism. J. Theor. Biol. 155:1992;215-242.
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0343035686
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Stoichiometric model of Escherichia coli metabolism: Incorporation of growth-rate dependent biomass composition and mechanistic energy requirements
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Pramanik J., Keasling J.D. Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. Biotechnol. Bioeng. 56:1997;398-421.
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Effect of Escherichia coli biomass composition on central metabolic fluxes predicted by a stoichiometric model
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