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Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools

DOI: 10.1371/journal.pcbi.1003195

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Abstract:

Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.

References

[1]  Buescher JM, Liebermeister W, Jules M, Uhr M, Muntel J, et al. (2012) Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism. Science (New York, NY) 335: 1099–1103 doi:10.1126/science.1206871.
[2]  Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150: 389–401 doi:10.1016/j.cell.2012.05.044.
[3]  Gerosa L, Sauer U (2011) Regulation and control of metabolic fluxes in microbes. Current opinion in biotechnology 22: 566–575 doi:10.1016/j.copbio.2011.04.016.
[4]  Shinar G, Feinberg M (2010) Structural sources of robustness in biochemical reaction networks. Science (New York, NY) 327: 1389–1391 doi:10.1126/science.1183372.
[5]  Heinrich R, Schuster S (1996) The Regulation of Cellular Systems. Springer.
[6]  Covert MW, Xiao N, Chen TJ, Karr JR (2008) Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics (Oxford, England) 24: 2044–2050 doi:10.1093/bioinformatics/btn352.
[7]  Bennett MR, Pang WL, Ostroff Na, Baumgartner BL, Nayak S, et al. (2008) Metabolic gene regulation in a dynamically changing environment. Nature 454: 1119–1122 Available: http://www.ncbi.nlm.nih.gov/pubmed/18668?041.
[8]  Gianchandani EP, Chavali AK, Papin JA (n.d.) The application of flux balance analysis in systems biology. Wiley interdisciplinary reviews Systems biology and medicine 2: 372–382. doi: 10.1002/wsbm.60
[9]  Orth JD, Thiele I, Palsson B? (2010) What is flux balance analysis? Nature Biotechnology 28: 245–248 doi:10.1038/nbt.1614.
[10]  Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nature reviews Microbiology 10: 291–305 doi:10.1038/nrmicro2737.
[11]  Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, et al. (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular systems biology 6: 390 doi:10.1038/msb.2010.47.
[12]  Teusink B, Passarge J, Reijenga CA, Esgalhado E, Van der Weijden CC, et al. (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. European Journal of Biochemistry 267: 5313–5329 doi:10.1046/j.1432-1327.2000.01527.x.
[13]  Kümmel A, Panke S, Heinemann M (2006) Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Molecular systems biology 2: 2006.0034 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=1681506&tool=pmcentrez?&rendertype=abstract.
[14]  Jamshidi N, Palsson B? (2008) Formulating genome-scale kinetic models in the post-genome era. Molecular systems biology 4: 171 Available: http://www.ncbi.nlm.nih.gov/pubmed/18319?723.
[15]  Bertsimas D, Tsitsiklis JN, Tsitsiklis J (1997) Introduction to Linear Optimization (Athena Scientific Series in Optimization and Neural Computation, 6). Athena Scientific
[16]  Savinell JM, Palsson BO (1992) Network analysis of intermediary metabolism using linear optimization. Journal of Theoretical Biology 154: 455–473 doi:10.1016/S0022-5193(05)80162-6.
[17]  Varma A, Boesch BW, Palsson BO (1993) Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Applied and environmental microbiology 59: 2465–2473.
[18]  Edwards JS, Ramakrishna R, Palsson BO (2002) Characterizing the Metabolic Phenotype: A Phenotype Phase Plane Analysis. Biotechnology 77: 27–36 doi:10.1002/bit.10047.
[19]  Warren P, Jones J (2007) Duality, Thermodynamics, and the Linear Programming Problem in Constraint-Based Models of Metabolism. Physical Review Letters 99: 1–4 Available: http://link.aps.org/doi/10.1103/PhysRevL?ett.99.108101.
[20]  Raman K, Chandra N (2009) Flux balance analysis of biological systems: Applications and challenges. Briefings in Bioinformatics 10: 435–449 doi:10.1093/bib/bbp011.
[21]  Boer VM, Crutchfield CA, Bradley PH, Botstein D, Rabinowitz JD (2010) Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations. Molecular biology of the cell 21: 198–211 doi:10.1091/mbc.E09-07-0597.
[22]  Mo ML, Palsson BO, Herrg?rd MJ (2009) Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC systems biology 3: 37 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=2679711&tool=pmcentrez?&rendertype=abstract.
[23]  Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16: 412–424 doi:10.1093/bioinformatics/16.5.412.
[24]  McCarl BA (1977) Degeneracy, Duality, and Shadow Prices in Linear Programming. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 25: 70–73 doi:10.1111/j.1744-7976.1977.tb02867.x.
[25]  Taymaz-Nikerel H, Van Gulik WM, Heijnen JJ (2011) Escherichia coli responds with a rapid and large change in growth rate upon a shift from glucose-limited to glucose-excess conditions. Metabolic engineering 13: 307–318 doi:10.1016/j.ymben.2011.03.003.
[26]  Taymaz-Nikerel H, De Mey M, Baart G, Maertens J, Heijnen JJ, et al. (2013) Changes in substrate availability in Escherichia coli lead to rapid metabolite, flux and growth rate responses. Metabolic engineering 16C: 115–129 doi:10.1016/j.ymben.2013.01.004.
[27]  Yuan J, Doucette CD, Fowler WU, Feng X-J, Piazza M, et al. (2009) Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli. Molecular systems biology 5: 302 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=2736657&tool=pmcentrez?&rendertype=abstract.
[28]  Xu Y-F, Amador-Noguez D, Reaves ML, Feng X-J, Rabinowitz JD (2012) Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase. Nature chemical biology 8: 562–568 doi:10.1038/nchembio.941.
[29]  Kochanowski K, Volkmer B, Gerosa L, R Haverkorn van Rijsewijk B, Schmidt A, et al. (2013) Functioning of a metabolic flux sensor in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America 110: 1130–1135 doi:10.1073/pnas.1202582110.
[30]  Becker SA, Palsson BO (2008) Context-Specific Metabolic Networks Are Consistent with Experiments. PLoS Computational Biology 4: e1000082 doi:10.1371/journal.pcbi.1000082.
[31]  Collins SB, Reznik E, Segrè D (2012) Temporal expression-based analysis of metabolism. PLoS computational biology 8: e1002781 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=3510039&tool=pmcentrez?&rendertype=abstract.
[32]  Tu BP, Kudlicki A, Rowicka M, McKnight SL (2005) Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science (New York, NY) 310: 1152–1158 Available: http://www.ncbi.nlm.nih.gov/pubmed/16254?148.
[33]  Tu BP, Mohler RE, Liu JC, Dombek KM, Young ET, et al. (2007) Cyclic changes in metabolic state during the life of a yeast cell. Proceedings of the National Academy of Sciences of the United States of America 104: 16886–16891 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=2040445&tool=pmcentrez?&rendertype=abstract.
[34]  Harcombe WR, Delaney NF, Leiby N, Klitgord N, Marx CJ (2013) The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum. PLoS Computational Biology 9: e1003091 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=3688462&tool=pmcentrez?&rendertype=abstract.
[35]  Oliveira AP, Sauer U (2012) The importance of post-translational modifications in regulating Saccharomyces cerevisiae metabolism. FEMS yeast research 12: 104–117 doi:10.1111/j.1567-1364.2011.00765.x.
[36]  Link H, Kochanowski K, Sauer U (2013) Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo. Nature Biotechnology advance on 31: 657–361 doi:10.1038/nbt.2489.
[37]  Scott M, Hwa T (2011) Bacterial growth laws and their applications. Current opinion in biotechnology 22: 559–565 doi:10.1016/j.copbio.2011.04.014.
[38]  Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and bioengineering 84: 647–657 Available: http://www.ncbi.nlm.nih.gov/pubmed/14595?777.
[39]  Imieliński M, Belta C, Halász A, Rubin H (2005) Investigating metabolite essentiality through genome-scale analysis of Escherichia coli production capabilities. Bioinformatics (Oxford, England) 21: 2008–2016 Available: http://www.ncbi.nlm.nih.gov/pubmed/15671?116.
[40]  Kim P-J, Lee D-Y, Kim TY, Lee KH, Jeong H, et al. (2007) Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proceedings of the National Academy of Sciences of the United States of America 104: 13638–13642 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=1947999&tool=pmcentrez?&rendertype=abstract.
[41]  Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America 99: 15112–15117 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=137552&tool=pmcentrez&?rendertype=abstract.
[42]  Brochado AR, Andrejev S, Maranas CD, Patil KR (2012) Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. PLoS computational biology 8: e1002758 doi:10.1371/journal.pcbi.1002758.
[43]  Schuetz R, Zamboni N, Zampieri M, Heinemann M, Sauer U (2012) Multidimensional optimality of microbial metabolism. Science (New York, NY) 336: 601–604 doi:10.1126/science.1216882.
[44]  Fell DA (1992) Metabolic control analysis: a survey of its theoretical and experimental development. The Biochemical journal 286 ((Pt 2) 313–330.
[45]  Voit EO, Radivoyevitch T (2000) Biochemical systems analysis of genome-wide expression data. Bioinformatics 16: 1023–1037 doi:10.1093/bioinformatics/16.11.1023.
[46]  Steuer R, Gross T, Selbig J, Blasius B (2006) Structural kinetic modeling of metabolic networks. Proceedings of the National Academy of Sciences of the United States of America 103: 11868–11873 Available: http://www.pubmedcentral.nih.gov/article?render.fcgi?artid=1524928&tool=pmcentrez?&rendertype=abstract.
[47]  Reznik E, Kaper TJ, Segrè D (2013) The dynamics of hybrid metabolic-genetic oscillators. Chaos: An Interdisciplinary Journal of Nonlinear Science 23: 013132 doi:10.1063/1.4793573.
[48]  Reed JL, Palsson B? (2004) Genome-Scale In Silico Models of E . coli Have Multiple Equivalent Phenotypic States: Assessment of Correlated Reaction Subsets That Comprise Network States. Genome Research 1797–1805 doi:10.1101/gr.2546004.reactions.
[49]  Gurobi Optimization Inc. (2012) Gurobi Optimizer Reference Manual: ww.gurobi.com.
[50]  Orth JD, Conrad TM, Na J, Lerman JA, Nam H, et al. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Molecular Systems Biology 7: 1–9 doi:10.1038/msb.2011.65.
[51]  Gutnick D, Calvo JM, Klopotowski T, Ames BN (1969) Compounds Which Serve as the Sole Source of Carbon or Nitrogen for Salmonella typhimurium LT-2. Journal of Bacteriology 100: 215–219.

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