The analysis of genetic regulatory networks will much benefit from the recent upscaling to the genomic level of experimental methods in molecular biology. In addition to high-throughput experimental methods, mathematical and bioinformatics approaches are indispensable for the analysis of genetic regulatory networks. Given the size and complexity of most networks of biological interest, an intuitive comprehension of their behavior is often difficult, if not impossible to obtain. A variety of methods for the modeling and simulation of genetic regulatory networks have been proposed in the literature. In this tutorial, the two principal approaches that have been used will be reviewed: methods based on differential equation models and stochastic models. In addition, we will indicate some alternative methods that have emerged in response to the difficulties encountered in applying the classical approaches.

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