System identification models for gene regulation system

Recent bio-technologies enable us to successfully discover the functional organization of a cell by simultaneously measuring the dynamic change of concentrations of thousands of bio-molecules after specific perturbation. Such available input-output data offer system identification great chances and meanwhile challenges to build mathematical models of the dynamic transcriptional control system, which is fundamentally important in systems biology. Here, we propose a general mathematical framework to describe the real world of transcriptional control system and the related system identification problems. Then some existing models are briefly introduced within this unified framework to infer gene regulatory networks or transcriptional regulatory network by incorporating prior information both on network structure and heterogeneous data sources.