Generating Data form the Evolution of Artificial Regulatory Networks

International audienceExisting regulatory network models attempt to copy the ``in vivo" regulatory principles by reproducing founded biological results ``in silico". These models sometimes don't reflect the biological principal of protein regulation and they don't take into account the organisms evolution. An innovative approach is presented on this contribution, based on the analyze of the existing models. Biological principles of regulatory networks have been considered. This new model can provide tools to study regulatory networks emergence and evolution and to acquire knowledge from generated time series. Studying networks in silico provide us the tools for controlling the environment and a better behavior analysis