Abstract
Kinetic models are used to describe cellular metabolism. Traditional models are based on enzymatic information obtained from in vitro experiments. In vitro data is inaccurate for in vivo modeling and is difficult to scale to large metabolic networks. Due to the impeding availability of metabolomic and fluxomic data types, we present an alternative kinetic modeling approach. Mass action stoichiometric simulation (MASS) models are scalable kinetic models that detail in vivo metabolic transformations. MASS formulation is a “middle-out” approach involving the use of a genome-scale metabolic network as a scaffold to map fluxomic and metabolomic measurements. Multiple binding states of enzymes can be explicitly added to account for regulatory effects. There are practical challenges with data completeness and quality of MASS models, but they do represent scalable kinetic models that exhibit biological properties such as time scale decomposition and account for regulation.
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References
Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ (2007) Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox. Nat Protocols 2: 727–738.
Bennett BD, Yuan J, Kimball EH, Rabinowitz JD (2008) Absolute quantitation of intracellular metabolite concentrations by an isotope ratio-based approach. Nature protocols 3: 1299–1311.
Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BO (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences of the United States of America 104: 1777–1782.
Edwards JS, Palsson BO (1999) Systems properties of the Haemophilus influenzae Rd metabolic genotype. Journal of Biological Chemistry 274: 17410–17416.
Famili I, Forster J, Nielsen J, Palsson BO (2003) Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale re-constructed metabolic network. Proceedings of the National Academy of Sciences of the United States of America 100: 13134–13139.
Famili I, Mahadevan R, Palsson BO (2005) k-Cone Analysis: Determining All Candidate Values for Kinetic Parameters on a Network Scale. Biophysical journal 88: 1616–1625.
Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular systems biology 3: 121.
Feist AM, Palsson BO (2008) The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotech 26: 659–667.
Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, Palsson BO (2010) Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metabolic engineering 12:3 173–186.
Forster J, Famili I, Fu PC, Palsson BO, Nielsen J (2003) Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network. Genome Research 13: 244–253.
Gianchandani EP, Joyce AR, Palsson BO, Papin JA (2009) Functional states of the genome-scale Escherichia coli transcriptional regulatory system. PLoS compu-tational biology 5: e1000403.
Irani MH, Maitra PK (1977) Properties of Escherichia coli mutants deficient in enzymes of glycolysis. Journal of bacteriology 132: 398–410.
Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing al-ternative drug targets. BMC systems biology 1: 26.
Jamshidi N, Palsson BO (2008) Top-down analysis of temporal hierarchy in biochemical reaction networks. PLoS computational biology 4: e1000177.
Jamshidi N, Palsson BO (2010) Mass Action Stoichiometric Simulation Models: Incorporating Kinetics and Regulation into Stoichiometric Models. Biophysical journal 98: 175–185.
Lueck JD, Fromm HJ (1974) Kinetics, mechanism, and regulation of rat skeletal muscle hexokinase. The Journal of biological chemistry 249: 1341–1347.
Maitra PK, Lobo Z (1971) A kinetic study of glycolytic enzyme synthesis in yeast. The Journal of biological chemistry 246: 475–488.
Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Molecular systems biology 5: 320.
Palsson BO (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, New York.
Raghunathan A, Reed J, Shin S, Palsson B, Daefler S (2009) Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. BMC systems biology 3: 38.
Thiele I, Palsson BO (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols 5: 93–121.
Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the War-burg effect: the metabolic requirements of cell proliferation. Science, New York, NY 324: 1029–1033.
Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology. Nature biotechnology 22: 1249–1252.
Yuan J, Bennett BD, Rabinowitz JD (2008) Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nature protocols 3: 1328–1340.
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Bordbar, A., Palsson, B.Ø. (2012). Moving Toward Genome-Scale Kinetic Models: The Mass Action Stoichiometric Simulation Approach. In: Koyutürk, M., Subramaniam, S., Grama, A. (eds) Functional Coherence of Molecular Networks in Bioinformatics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0320-3_8
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DOI: https://doi.org/10.1007/978-1-4614-0320-3_8
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