title: Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models.
abstract: Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design.
authors.affiliation: Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.|Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.|SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Yuan
authors.affiliation: Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
authors.forename: Teng
authors.identifier: http://orcid.org/0000-0001-8323-6163
authors.initials: T
authors.lastname: Huang
authors.affiliation: Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
authors.forename: Peishun
authors.initials: P
authors.lastname: Li
authors.affiliation: College of Life Sciences, Tianjin Normal University, Tianjin, China.
authors.forename: Tong
authors.initials: T
authors.lastname: Hao
authors.affiliation: Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.|Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.|SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.
authors.forename: Feiran
authors.initials: F
authors.lastname: Li
authors.affiliation: Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.|Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.|School of Informatics, the University of Edinburgh, Informatics Forum, Edinburgh, United Kingdom.
authors.forename: Hongwu
authors.initials: H
authors.lastname: Ma
authors.affiliation: Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.|SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.
authors.forename: Zhiwen
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.|SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.
authors.forename: Xueming
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, P. R. China.|SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Chen
authors.affiliation: Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.|School of Informatics, the University of Edinburgh, Informatics Forum, Edinburgh, United Kingdom.
authors.forename: Igor
authors.initials: I
authors.lastname: Goryanin
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics* / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D032921:Consensus; D015964:Gene Expression Regulation, Bacterial; D016680:Genome, Bacterial; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D016958:Pseudomonas putida / Q000235:genetics / Q000378:metabolism*
chemical_list: D001426:Bacterial Proteins