MPI für Dynamik komplexer technischer Systeme / Systems Biology |
|Thermodynamically-Consistent Reduced-Order Modeling of the Oxygen Response of Escherichia coli|
|Authors:||Ederer, M.; Sauter, T.; Bettenbrock, K.; Sawodny, O.; Gilles, E. D.|
|Name of Conference/Meeting:||9th International Conference on Systems Biology (ICSB 2008)|
|Place of Conference/Meeting:||Göteborg, Sweden|
|(Start) Date of Conference/Meeting|
|End Date of Conference/Meeting |
| Invitation status:||contributed|
|Abstract / Description:||Objective: The response of Escherichia coli to a varying oxygen availability is mediated by several genetic and enzymatic regulation systems. The adaptation to a change in oxygen availability involves a major reorganization of metabolic fluxes and concentrations. Thus, a consistent understanding of the oxygen response cannot be achieved by a reductionist's approach only. A computational model of the oxygen response can provide us with a sound understanding of the interaction of the involved processes.
Results: We built a computational model of the steady-state oxygen response of Escherichia coli in a glucose limited chemostat. It contains the glycolysis, the tricarboxylic acid cycle, the electron transport chain and several fermentative pathways. Further, it contains the genetic regulation by the transcription factors ArcA, FNR, PdhR and CRP. In order to guarantee the thermodynamic feasibility of the model, we use the recently developed thermodynamic-Kinetic Modeling formalism (Ederer & Gilles, Biophys J, 92 (6), 2007). To reduce the number of state variables and parameters we extensively apply rapid-equilibrium assumptions to the metabolic reactions that are know to proceed near equilibrium. The model parameters were adapted to fit experimental data (uptake
and excretion fluxes, intracellular nadh/nad ratio and several mRNA and enzyme activities, Alexeeva et al., J Bact, 2000, 2002, 2003).
Conclusions: The model predicts intracellular fluxes and concentrations as well as the activity of the transcription factors for varying oxygen availability. Further, with the model we can perform extensive in silico mutant studies. The above introduced computational model provides a coherent and global picture of the oxygen response of E. coli. In the future the SUMO consortium will refine the model by testing several of the model predictions on mutant behavior.
Acknowledgements: This work was supported by SysMO; project number 3 (Systems Understanding of Microbial Oxygen
Responses, SUMO); www.sysmo.net. ME acknowledges also support from the German Bundesministerium für Bildung und
Forschung (BMBF, FORSYS initiative). We thank all members of SUMO for fruitful discussions.
© 2008 University of Göteborg
[accessed December 22, 2008]
|Document Type:||Talk at Event|
|Communicated by:||Ernst-Dieter Gilles|
|Affiliations:||MPI für Dynamik komplexer technischer Systeme/Systems Biology|
|External Affiliations:||Institute for System Dynamics, Pfaffenwaldring 9, 70569 Stuttgart|
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