Effective Models of Periodically Driven Networks

• Published in: Biophysical journal Vol. 101; no. 11; pp. 2563 - 2571
• Main Authors: Shulman, Jason, Seemann, Lars, Gunaratne, Gemunu H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 07.12.2011; Biophysical Society; The Biophysical Society
• Subjects: Animals; Biological Systems and Multicellular Dynamics; Circadian rhythm; Circadian Rhythm - genetics; Circadian Rhythm - physiology; Circadian rhythms; Gene expression; Gene Expression Regulation; Gene Knockout Techniques; Gene Regulatory Networks - genetics; Genes; Heart; Mammals; Mammals - genetics; Mammals - physiology; microarray technology; Models, Genetic; mutants; Mutation; Mutation - genetics
• ISSN: 0006-3495; 1542-0086; 1542-0086
• DOI: 10.1016/j.bpj.2011.10.008

Circadian rhythms are governed by a highly coupled, complex network of genes. Due to feedback within the network, any modification of the system's state requires coherent changes in several nodes. A model of the underlying network is necessary to compute these modifications. We use an effective modeling approach for this task. Rather than inferred biochemical interactions, our method utilizes microarray data from a group of mutants for its construction. With simulated data, we develop an effective model for a circadian network in a peripheral tissue, subject to driving by the suprachiasmatic nucleus, the mammalian pacemaker. The effective network can predict time-dependent gene expression levels in other mutants.