Systems biological analysis of complex biochemical networks of E. coli

Abstract

Systems biology aims at understanding the entire picture of the various networks in biological systems by integrating information generated from high-throughput experiments and computational modeling and simulation. Recent advances in ‘omics’ technologies and systems biology have brought the systems approach to metabolic engineering, which can be defined as purposeful modification of metabolic and cellular networks by employing various experimental techniques. Metabolic flux analysis in the metabolic engineering is used for analyzing cell physiology through estimating intracellular flux distributions and becomes a powerful prediction tool in conjunction with genome-scale stoichiometric models. Constraints-based flux analysis, included in the metabolic flux analysis, utilizes optimization techniques to simulate large genome-scale stoichiometric models with various objectives which can be maximization of cell growth rate, maximization of product formation, and minimization of byproduct formation. In order to interpret and examine the metabolic behavior in response to genetic and/or environmental modifications, we have developed MetaFluxNet which is a stand-alone program package for the management of metabolic reaction information and quantitative metabolic flux analysis. It is updated by extended modules, such as SBML module, export module and multiobjective flux balance analysis module. This study proposes a novel multiobjective flux balance analysis method, which adapts the noninferior set estimation (NISE) method for multiobjective linear programming (MOLP) problems. NISE method can generate an approximation of the Pareto curve for conflicting objectives without redundant iterations of single objective optimization. Furthermore, the flux distributions at each Pareto optimal solution can be obtained for understanding the internal flux changes in the metabolic network. To overcome the difficulty of hierarchal approach and to save time and computational p...