| Resumo: | 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, for building a genome-scale kinetic model of Escherichia coli metabolism. This workflow was evaluated for the condensed version of a genome-scale network of E. coli (Orth et al., 2010). The presented approach appears to be a promising mechanism for detailed kinetic modeling at the genome-scale of the metabolism of other organisms.
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