Effective Models of Periodically Driven Networks
| Autor: | Jason Shulman, Lars Seemann, Gemunu H. Gunaratne |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Gene regulatory network
Biophysics Biology 03 medical and health sciences Gene Knockout Techniques 0302 clinical medicine Animals Gene Regulatory Networks Circadian rhythm 030304 developmental biology Genetics Regulation of gene expression Mammals 0303 health sciences Models Genetic Microarray analysis techniques Suprachiasmatic nucleus Complex network Biological Systems and Multicellular Dynamics Circadian Rhythm Gene Expression Regulation Mutation State (computer science) Biological system 030217 neurology & neurosurgery |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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