An approach towards genome-scale kinetic modelling: application to the Escherichia coli metabolism

Abstract

Understanding the dynamic behavior of living organisms is a great challenge in systems biology. To address this, computational dynamic modeling of metabolic networks is essential to guide experimentation and to explain properties of complex biological systems. Large-scale kinetic models at the reaction network level are usually constructed using mechanistic enzymatic rate equations and a large number of kinetic parameters. However, two of the biggest obstacles to construct accurate dynamic models are model complexity and limited in vivo kinetic information. In the present work, we test an alternative strategy with a relatively small number of kinetic parameters composed by the approximated lin-log kinetics, coupled with a constraint-based method and a priori model reduction based on time scale analysis and a conjunctive fusion approach (Machado et al., 2010).. This workflow was evaluated for the condensed version of a genome-scale kinetic model of Escherichia coli metabolism (Orth et al., 2010). The presented approach seems to be a promising mechanism for detailed kinetic modeling even at the genome-scale of the metabolism of other organisms.