title: Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism.
abstract: An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-'omics' steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.
authors.affiliation: Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
authors.forename: Ahmad A
authors.initials: AA
authors.lastname: Mannan
authors.affiliation: Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan; Institute for Advanced Biosciences, Keio University, Nipponkoku 403-1, Daihouji, Tsuruoka, Yamagata, 997-0017, Japan.
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Toya
authors.affiliation: Institute for Advanced Biosciences, Keio University, Nipponkoku 403-1, Daihouji, Tsuruoka, Yamagata, 997-0017, Japan; Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, Fukuoka, 820-8502, Japan.
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Shimizu
authors.affiliation: Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
authors.forename: Johnjoe
authors.initials: J
authors.lastname: McFadden
authors.affiliation: Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
authors.forename: Andrzej M
authors.initials: AM
authors.lastname: Kierzek
authors.affiliation: Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
authors.forename: Andrea
authors.initials: A
authors.lastname: Rocco
mesh_terms: D002244:Carbon / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism*; D016680:Genome, Bacterial / Q000235:genetics; D023281:Genomics; D007700:Kinetics; D064688:Metabolic Flux Analysis; D008954:Models, Biological
chemical_list: D002244:Carbon