Inference approaches of genetic regulatory networks

Models to describe a genetic regulatory network can be extremely complicated and we need significant amount of experimental data to infer the parameters of a detailed model. Selection of a model to represent a genetic regulatory network depends on the purpose of modeling, available experimental data and computational complexity of the model. Numerous approaches have been proposed for inference of genetic regulatory networks. Without gold standard on actual biological networks, the performance of these approaches are hard to benchmark. Some approaches may be suitable for specific analysis whereas they can perform poorly for another analysis. Inference of a genetic regulatory network model from time series perturbation data still remains a big challenge. In this thesis, we review several current existed inference approaches and discuss their appropriateness for modeling of genetic regulatory networks. We will further apply few of the inference approaches to our actual experimental time series perturbation data of human cancer cell lines.