The availability of genome-scale metabolic networks has accelerated the development of methods to analyse system-wide metabolic properties. A fundamental aim of systems biology is to predict cellular behaviours in silico by examining the dynamics of cellular processes [6]. As a ABT 199 result, it is necessary to

go beyond static constraint-based models and build kinetic models where systems can be perturbed [7]. However, it is time-consuming and costly to experimentally measure all metabolite concentrations, reaction fluxes and kinetic parameters at the genome scale. This has led to recent efforts to providing methods to build kinetic models using other approaches, such as linlog kinetics [8,9], generic Inhibitors,research,lifescience,medical equations [10], parameter balancing [11] and convenience

kinetics [12]. Reverse engineering is often used in systems biology to reconstruct biological Inhibitors,research,lifescience,medical interactions and constrain kinetic parameter values from experimental data [13]. It is often unlikely to have access to comprehensive datasets comprising all metabolic, genomic and proteomic data needed to fully constrain kinetic parameter values, and as such, simulated or calculated data may be used as a substitute. Flux Balance Analysis (FBA), which enables the calculation of an optimal flux distribution using linear programming, has proved an efficient method to represent metabolic phenotypes Inhibitors,research,lifescience,medical under various experimental conditions, with successful prediction rates found to be approximately 60 and 86% for H. pylori and E. coli respectively in gene deletion studies [14]. As kinetic parameters are not required for Inhibitors,research,lifescience,medical FBA, a flux distribution can be calculated in a genome-scale metabolic model

when only the network stoichiometry and flux constraints are known. Inhibitors,research,lifescience,medical In Lubitz et al. [11] the authors used a technique known as ‘parameter balancing’, which is based on Bayesian parameter estimation, to estimate kinetic parameters of metabolic reactions. This method was validated on the phosphofructokinase reaction but may prove challenging to generalise to the genome tuclazepam scale. Current methods also often omit flux distributions from the input data, which has the caveat that reaction fluxes may be estimated to zero even in a non-equilibrium setting. The model building approach presented in Adiamah et al. [7] showed that estimating kinetic parameters using metabolic and flux data could successfully reproduce experimental conditions under both steady-state and dynamic conditions. In an attempt to develop a solution addressing the combined challenges of building genome-scale integrative kinetic models, estimating kinetic parameters and measuring redundancy, we here present an approach to build a genome-scale kinetic model using generic equations, given a genome-scale flux distribution derived from FBA.