title: Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production.
abstract: In the post-genome era, it is one challenge to understand the cellular metabolism at the systematic levels. Mathematical modeling of microorganisms and subsequent computer simulation are effective tools for systems biology. In this paper, based on the genome-scale Escherichia coli stoichiometric model iJR904, through the GAMS linear programming package, the in silico maximal succinate yield was estimated to be 1.714 mol/mol glucose. When another two constraints were added, the maximal succinate yield dropped to 1.60 mol/mol glucose. Further analysis substantiated the uniqueness of the flux distribution under such constraints. After comparisons with the metabolic flux analysis (MFA) results computed from the wet experimental data of the three kinds of E. coli, three potential improvement target sites, the glucose phosphotransferase transport system, the pyruvate carboxylase, and the glyoxylate shunt, were identified and selected for the genetic modifications. All the three genetic modified strains showed increased succinate yield. The final strain TUQ19/pQZ6 had a high yield of 1.29 mol succinate/mol glucose and high productivity. The success of the above experiments proved that this in silico optimal succinate production pathway is reasonable and practical. This method may also be used as a general strategy to help enhance the yields of other favorable metabolites in E. coli.
authors.affiliation: Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, People's Republic of China.
authors.forename: Qingzhao
authors.initials: Q
authors.lastname: Wang
authors.forename: Xun
authors.initials: X
authors.lastname: Chen
authors.forename: Yudi
authors.initials: Y
authors.lastname: Yang
authors.forename: Xueming
authors.initials: X
authors.lastname: Zhao
mesh_terms: D000693:Anaerobiosis; D019295:Computational Biology; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000235:genetics; D017353:Gene Deletion; D016680:Genome, Bacterial; D005947:Glucose / Q000378:metabolism; D006038:Glyoxylates / Q000378:metabolism; D053858:Metabolic Networks and Pathways / Q000235:genetics* / Q000502:physiology; D008954:Models, Biological; D010731:Phosphoenolpyruvate Sugar Phosphotransferase System / Q000235:genetics / Q000378:metabolism; D011766:Pyruvate Carboxylase / Q000235:genetics / Q000378:metabolism; D012097:Repressor Proteins / Q000235:genetics; D019802:Succinic Acid / Q000378:metabolism*; D014157:Transcription Factors / Q000235:genetics
chemical_list: D029968:Escherichia coli Proteins; D006038:Glyoxylates; D012097:Repressor Proteins; D014157:Transcription Factors; C067479:IclR protein, E coli; D019802:Succinic Acid; D010731:Phosphoenolpyruvate Sugar Phosphotransferase System; C031629:phosphoenolpyruvate-glucose phosphotransferase; D011766:Pyruvate Carboxylase; D005947:Glucose