The perhaps most important signaling network in living cells is constitutedby the interactions of proteins with the genome – the regulatory geneticnetwork of the cell. From a system-level point of view, the variousinteractions and control loops, which form a genetic network, represent thebasis upon which the vast complexity and flexibility of life processesemerges. Here we provide a review over some efforts towards gaining aquantitative understanding of regulatory genetic networks by means of largescale computational models. After a brief description of the biologicalprinciples of gene regulation, we summarize recent advances in massivegene-expression measurements by DNA-microarrays, which form the to date mostpowerful data basis for models of genetic networks. One class of models suchas reaction-diffusion networks and nonlinear dynamical descriptions arebiased towards using explicit molecular biological knowledge. A secondclass, centered around machine learning approaches like neural networks andBayesian networks, adopts a more data-driven approach and thereby makesmassive use of the novel gene expression measurement techniques.