Genetic regulatory networks consist of genes, proteins and metabolites whose mutual interactions control the spatiotemporal expression of genes in an organism. An understanding of the function of an organism can be obtained by elucidating these networks. The study of genetic regulatory networks has taken a qualitative leap through the use of modern genomic techniques. However, quantitative information on kinetic parameters and molecular concentrations is only seldom available, even in the case of well-studied model systems. To cope with these constraints a qualitative modelling and simulation method has been developed and implemented in the computer tool Genetic Network Analyzer (GNA, available at http://www-helix.inrialpes.fr/gna) [1]. This approach is based on a class of piecewise-linear differential equation models [2,3], and has been validated in the analysis of the well-studied genetic regulatory network controlling the initiation of sporulation in Bacillus subtilis [4]. We have begun to study a genetic regulatory system whose functioning is less well understood, the nutritional stress response in Escherichia coli. In this bacterium, the adaptation to nutritional stress is under control of a complicated network of global regulators, which allows the cells to adjust their metabolism and growth in response to nutrient limitation [5]. The main components of our qualitative model are: the genes involved in DNA supercoiling, the genes encoding two pleiotropic transcriptional factors, the protein Fis and the complex cAMP.CRP, and the genes encoding the ribosomal RNAs, whose expression is representative of the cell's growth state [5,6]. Dynamical simulations show how the control of the expression and activity of these genes allows E. coli to adapt to a nutritional stress. Experimental validation of the model predictions is currently under way.
References
[1] de Jong et al., 2003, Bioinformatics, 19(3):336-344
[2] Glass et Kauffman, 1973, J. Theor. Biol., 39(1):103-29.
[3] de Jong et al., 2004, Bull. Math. Biol., 66(2):301-340
[4] de Jong et al., 2004, Bull. Math. Biol., 66(2):261-300
[5] Hengge-Aronis, 2000, Bacterial Stress Responses, G. Storz and R. Hengge-Aronis (eds.), ASM Press, Washington D.C, pp. 161-178
[6] Schneider et al., 2003, Curr. Opin. Microbiol., 6:151-156
