Modeling gene regulatory networks by incremental evolution and system decomposition

Constructing genetic regulatory networks from expression data is one of the most important issues in system biology research. However, building regulatory models manually is a tedious task, especially when the number of genes involved increases with the complexity of regulation. To automate the procedure of network construction, we develop a methodology to infers-systems as regulatory systems. Our work also deals with the scalability problem by an incremental evolution strategy and a network decomposition method with several data analysis techniques. To verify the presented approaches, experiments have been conducted and the results show that they can be used to infer gene regulatory networks successfully.