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Mathematical Modelling of the Regulation of the Stress Sigma Factor σS in Escherichia coli

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Backfisch,  T.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Gilles,  E. D.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Citation

Backfisch, T., Pruteanu, M., Hengge, R., & Gilles, E. D. (2004). Mathematical Modelling of the Regulation of the Stress Sigma Factor σS in Escherichia coli. Poster presented at 5th International Conference on Systems Biology (ICSB 2004), Heidelberg, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9D9A-4
Abstract
Motivation: Regulation of the sigma subunit σS (RpoS) is the central part of the general stress response in E. coli. σS is controlled by a number of diverse stresses that affect its synthesis (transcription + translation) as well as its degradation and thereby lead to accumulation of the sigma subunit. σS can then reprogram RNA polymerase and initiate transcription of specific sets of target genes. Whereas quite a number of regulators influencing σS level are known (1), the molecular connections between these regulators and σS / the invoking stress signals often remain elusive. Thus mathematical modeling is needed to come up with proposals for the open questions. Model and Simulation Results: Here a dynamical model for regulation of σS is presented that is based on a hierarchichal modeling concept ([2]). The regulatory signals are transduced from top to lower levels but not vice versa, which makes it easier to identify submodules and to understand the behavior of the system. The model consists of a differential algebraic equation system. The kinetic parameters are estimated from experimental data. With the model the influence of certain regulators and regulatory structures are analyzed. For example the response regulator RssB is a main regulator of proteolysis of σS and is itself a σS responsive gene, thereby establishing a homeostatic feedback loop. The model can show that this feature enlarges the operating rage of σS proteolysis (as observed in experiments, [3]). Furthermore the model shows that this feature also speeds up the dynamics of σS induction considerably. References: [1] R. Hengge Aronis, Microbiology and Molecular Biology Reviews (2002), 66 (3), 373-395. [2] A. Kremling and E. D. Gilles, Metabolic Engineering (2001), 3 (2), 138-150. [3] M. Pruteanu and R. Hengge-Aronis, Molecular Microbiology (2002), 45 (6), 1701-1713.