Large-scale computational modeling of genetic regulatory networks

The perhaps most important signaling network in living cells is constituted by the interactions of proteins with the genome-the regulatory genetic network of the cell. From a system-level point of view, the various interactions and control loops, which form a genetic network, represent the basis upon which the vast complexity and flexibility of life processes emerges. Here we provide a review over some efforts towards gaining a quantitative understanding of regulatory genetic networks by means of large scale computational models. After a brief description of the biological principles of gene regulation, we summarize recent advances in massive gene-expression measurements by DNA-microarrays, which form the to date most powerful data basis for models of genetic networks. One class of models such as reaction-diffusion networks and nonlinear dynamical descriptions are biased towards using explicit molecular biological knowledge. A second class, centered around machine learning approaches like neural networks and Bayesian networks, adopts a more data-driven approach and thereby makes massive use of the novel gene expression measurement techniques.