Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models

BMC Bioinformatics

Cameron Cotten¹  Jennifer L Reed¹ 

[1] DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1415 Engineering Dr, Madison, WI 53706, USA

关键词: FBA;    Constraint-based;    Central metabolism;    Kinetics;    Metabolic engineering;   

Others  :  1088002
DOI  :  10.1186/1471-2105-14-32

 received in 2012-04-18, accepted in 2013-01-16,  发布年份 2013

【 摘 要 】

Background

Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used.

Results

In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism.

Conclusions

This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.

【 授权许可】

   
2013 Cotten and Reed; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004, 2(11):886-897.
• [2]Orth JD, Thiele I, Palsson BO: What is flux balance analysis? Nat Biotechnol 2010, 28(3):245-248.
• [3]Schuetz R, Kuepfer L, Sauer U: Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 2007, 3:119.
• [4]Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO: Integrating high-throughput and computational data elucidates bacterial networks. Nature 2004, 429(6987):92-96.
• [5]Chandrasekaran S, Price ND: Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 2010, 107(41):17845-17850.
• [6]Shlomi T, Eisenberg Y, Sharan R, Ruppin E: A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol 2007, 3:101.
• [7]Kümmel A, Panke S, Heinemann M: Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2006, 2:2006-0034.
• [8]Henry CS, Broadbelt LJ, Hatzimanikatis V: Thermodynamics-based metabolic flux analysis. Biophys J 2007, 92(5):1792-1805.
• [9]Hoppe A, Hoffmann S, Holzhutter HG: Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Syst Biol 2007, 1:23. BioMed Central Full Text
• [10]Henry CS, Jankowski MD, Broadbelt LJ, Hatzimanikatis V: Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys J 2006, 90(4):1453-1461.
• [11]Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar-Joseph Z, Barabasi AL, Oltvai ZN: Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci USA 2007, 104(31):12663-12668.
• [12]Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T: Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 2010, 26(12):i255-260.
• [13]Hanly TJ, Henson MA: Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures. Biotechnol Bioeng 2010, 108(2):376-385.
• [14]Mahadevan R, Edwards JS, Doyle FJ 3rd: Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 2002, 83(3):1331-1340.
• [15]Zhuang K, Izallalen M, Mouser P, Richter H, Risso C, Mahadevan R, Lovley DR: Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 2010, 5(2):305-316.
• [16]Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M, Hirai K, Hoque A, et al.: Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 2007, 316(5824):593-597.
• [17]Chassagnole C, Noisommit-Rizzi N, Schmid JW, Mauch K, Reuss M: Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol Bioeng 2002, 79(1):53-73.
• [18]Palsson B: Systems biology: properties of reconstructed networks. Cambridge; New York: Cambridge University Press; 2006.
• [19]Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO: A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol Syst Biol 2011, 7:535.
• [20]Chapman AG, Fall L, Atkinson DE: Adenylate energy charge in Escherichia coli during growth and starvation. J Bacteriol 1971, 108(3):1072-1086.
• [21]Goldberg RN, Tewari YB, Bhat TN: Thermodynamics of enzyme-catalyzed reactions–a database for quantitative biochemistry. Bioinformatics 2004, 20(16):2874-2877.
• [22]Antoniewicz MR, Kelleher JK, Stephanopoulos G: Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metab Eng 2006, 8(4):324-337.
• [23]Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D, et al.: Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 2010, 6:390.
• [24]Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP: Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 2007, 3(10):1871-1878.
• [25]Van Eunen K, Kiewiet JA, Westerhoff HV, Bakker BM: Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics. PLoS Comput Biol 2012, 8(4):e1002483.
• [26]Huang X, Holden HM, Raushel FM: Channeling of substrates and intermediates in enzyme-catalyzed reactions. Annu Rev Biochem 2001, 70:149-180.
• [27]Ellis RJ: Macromolecular crowding: obvious but underappreciated. Trends Biochem Sci 2001, 26(10):597-604.
• [28]Wang L, Birol I, Hatzimanikatis V: Metabolic control analysis under uncertainty: framework development and case studies. Biophys J 2004, 87(6):3750-3763.
• [29]Garrett R, Grisham CM: Biochemistry. Fort Worth: Saunders College Pub; 1995.
• [30]Klimacek M, Krahulec S, Sauer U, Nidetzky B: Limitations in xylose-fermenting Saccharomyces cerevisiae, made evident through comprehensive metabolite profiling and thermodynamic analysis. Appl Environ Microbiol 2010, 76(22):7566-7574.