Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks

Background: A gene-regulatory network (GRN) refers to DNA segments that interact through their RNA and protein products and thereby govern the rates at which genes are transcribed. Creating accurate dynamic models of GRNs is gaining importance in biomedical research and development. To improve our understanding of continuous deterministic modeling methods employed to construct dynamic GRN models, we have carried out a comprehensive comparative study of three commonly used systems of ordinary differential equations: The S-system (SS), artificial neural networks (ANNs), and the general rate law of transcription (GRLOT) method. These were thoroughly evaluated in terms of their ability to replicate the reference models' regulatory structure and dynamic gene expression behavior under varying conditions. Results: While the ANN and GRLOT methods appeared to produce robust models even when the model parameters deviated considerably from those of the reference models, SS-based models exhi...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Martin T. Swain, Johannes J. Mandel, Werner Dubitzky
• Keywords: BMCBI 2010, Dynamic Grn Models, Dynamic Models, Reference Models