Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism
|Title:||Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism||Authors:||Fleming, R.M.T.
|Permanent link:||http://hdl.handle.net/10197/4994||Date:||Jun-2010||Online since:||2013-11-28T14:48:35Z||Abstract:||The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in Escherichia coli and compare favourably with in silico prediction by flux balance analysis.||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Journal of Theoretical Biology||Volume:||264||Issue:||3||Start page:||683||End page:||692||Copyright (published version):||2010 Elsevier||Keywords:||Systems biology; Constraint-based modelling; Linear polytope; Logarithmic polytope; Algebraic geometry||DOI:||10.1016/j.jtbi.2010.02.044||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||SBI Research Collection|
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