Modern signals and systems
Automatica (Journal of IFAC)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The identification of dynamic gene-protein networks
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Qualitative analysis of nonlinear biochemical networks with piecewise-affine functions
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Proving stabilization of biological systems
VMCAI'11 Proceedings of the 12th international conference on Verification, model checking, and abstract interpretation
Analytical Solution of Steady-State Equations for Chemical Reaction Networks with Bilinear Rate Laws
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.