How to infer the interactive large scale regulatory network in 'omic' studies

Inferring regulatory networks in genetic systems and metabolic pathways is one of the most important problems in systems biology. Inferring network structure from experimentally observed time series data is an inverse problem. To deal with such problems, we have developed an efficient numerical optimization method called the hybrid method, which is a combination of real-coded genetic algorithms and the modified Powell method using the S-system representation. In general, a large regulatory network comprises numerous interactive system components and requires the optimization of a large number of parameters with non-zero interaction coefficients between them. To date, we have succeeded in optimizing 272 real-valued parameters using the hybrid method. Although compared with conventional numerical optimization methods, the hybrid method is powerful but is still insufficient for inferring large-scale networks. Here we discuss the inference of interactive large-scale regulatory networks in 'omics' studies based on our hybrid numerical optimization method.