Abstract

The adaptation of microorganisms to their environment is controlled at the molecular level by large and complex networks of biochemical reactions involving genes, RNAs, proteins, metabolites, and small signalling molecules. In theory, it is possible to write down mathematical models of these networks, and study these by means of classical analysis and simulation tools. In practice, this is not easy to achieve though, as quantitative data on kinetic parameters are usually absent for  most  systems  of  biological  interest. Moreover,  the  models  consist of  a  large number  of  variables,  are  strongly  nonlinear  and  include  different  time-scales, which make  them difficult to handle both mathematically and computationally.

We have developed methods for the reduction and approximation of kinetic models of bacterial regulatory networks to simplified, so-called piecewise-linear differential equation models. The qualitative dynamics of the piecewise-linear models can be studied using discrete abstractions from hybrid systems theory. This enables the application of model-checking tools to the formal verification of dynamic properties of the regulatory networks. The above approach has been implemented in the publicly-available computer tool Genetic Network Analyzer (GNA) and has been used to analyze a variety of bacterial regulatory networks.

I will illustrate the application of GNA by means of the network of global transcription regulators controlling the adaptation of the bacterium Escherichia coli to environmental stress conditions. Even though E. coli is one of the best studied model organisms, it is currently little understood how a stress signal is sensed and propagated through the network of global regulators, and leads the cell to respond in an adequate way. Qualitative modeling and simulation of the network of global regulators has allowed us to identify essential features of the transition between exponential and stationary phase of the bacteria and to make new predictions on the dynamic behavior following a carbon upshift.

CV

Hidde de Jong obtained MSc degrees in Computer Science, Philosophy of Science, and Management Science from the University of Twente (the Netherlands) and completed a PhD thesis in Computer Science at the same university. He joined INRIA in 1998 and is currently a senior research scientist in the bioinformatics and biological modeling group at INRIA Grenoble-Rhône-Alpes. He has been a member of the editorial board of the IEEE/ACM Transactions on Computational Biology and Bioinformatics and the Journal of Mathematical Biology.

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