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Issue No. 01 - January-February (2011 vol. 8)
ISSN: 1545-5963
pp: 166-181
Delphine Ropers , INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex
Valentina Baldazzi , INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex
Hidde de Jong , INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex
The adaptation of the bacterium Escherichia coli to carbon starvation is controlled by a large network of biochemical reactions involving genes, mRNAs, proteins, and signalling molecules. The dynamics of these networks is difficult to analyze, notably due to a lack of quantitative information on parameter values. To overcome these limitations, model reduction approaches based on quasi-steady-state (QSS) and piecewise-linear (PL) approximations have been proposed, resulting in models that are easier to handle mathematically and computationally. These approximations are not supposed to affect the capability of the model to account for essential dynamical properties of the system, but the validity of this assumption has not been systematically tested. In this paper, we carry out such a study by evaluating a large and complex PL model of the carbon starvation response in E. coli using an ensemble approach. The results show that, in comparison with conventional nonlinear models, the PL approximations generally preserve the dynamics of the carbon starvation response network, although with some deviations concerning notably the quantitative precision of the model predictions. This encourages the application of PL models to the qualitative analysis of bacterial regulatory networks, in situations where the reference time scale is that of protein synthesis and degradation.
Bacterial regulatory networks, piecewise-linear differential equations, ordinary differential equations, model reduction, Escherichia coli, stress response, quasi-steady-state approximation.

D. Ropers, V. Baldazzi and H. de Jong, "Model Reduction Using Piecewise-Linear Approximations Preserves Dynamic Properties of the Carbon Starvation Response in Escherichia coli," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 166-181, 2009.
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