Dataset Information

Kotte2010_Ecoli_Metabolic_Adaption

ABSTRACT: This is the model described in: Bacterial adaptation through distributed sensing of metabolic fluxes Oliver Kotte, Judith B Zaugg and Matthias Heinemann;Mol Sys Biol2010;6:355. doi:10.1038/msb.2010.10; Abstract: The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources. In its current form this SBML model is parametrized for the glucose to acetate transition and to simulate the extended diauxic shift as shown in figure 3 and scenario 6 of the attached matlab file. In this scenario the cells first are grown from an OD600 (BM) of 0.03 with a starting glucose concentration of 0.5 g/l for 8.15 h (29340 sec). Then a medium containing 5 g/l acetate is inoculated with these cells to an OD600 of 0.03 and grown for another 19.7 hours (70920 sec). Finally the cells are shifted to a medium containing both glucose and acetate at a concentration of 3 g/l with a starting OD600 of 0.0005. The shifts where implemented using events triggering at the times determined by the parameters shift1 and shift2 (in hours). To simulate other scenarios the initial conditions need to be changed as described in the supplemental materials (supplement 1) The original SBML model and the MATLAB file used for the calculations can be down loaded as supplementary materials of the publication from the MSB website. (supplement 2). The units of the external metabolites are in [g/l], those of the biomass in optical density,OD600, taken as dimensionless, and [micromole/(gramm dry weight)] for all intracellular metabolites. As the latter cannot be implemented in SBML, it was chosen to be micromole only and the units of the parameters are left mostly undefined. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

SUBMITTER: Lukas Endler

PROVIDER: BIOMD0000000244 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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Bacterial adaptation through distributed sensing of metabolic fluxes.

Kotte Oliver O Zaugg Judith B JB Heinemann Matthias M

Molecular systems biology 20100309

The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we ...[more]

PMID: 20212527

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