Abstract

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can’t be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

Highlights

  • Under pseudo-steady state condition (PSSC), the resulting smallest balanced pathways (SBPs) of our method can well satisfy the stoichiometric constraints and non-decomposability condition; Multiple pathways which meet the above-mentioned criteria can be found and provided as candidate design; In addition, high yield is a new function; Especially, thermodynamics feasibility has been considered in our approach

  • We computed out the smallest balanced pathways from glucose to succinic acid in the genomescale metabolic network of E. coli

  • Our algorithm has a number of good features: 1) It is a method of thorough stoichiometry

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Summary

Introduction

By using MIP (Mixed Integer Programming) strategy we put forward an algorithm to identify the smallest balanced pathways (SBPs) which convert the source compound to the target compound in large-scale metabolic networks. Under PSSC, the resulting SBPs of our method can well satisfy the stoichiometric constraints and non-decomposability condition; Multiple pathways which meet the above-mentioned criteria can be found and provided as candidate design; In addition, high yield is a new function; Especially, thermodynamics feasibility has been considered in our approach.

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