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Modeling stochasticity and variability in gene regulatory networks

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dc.contributor.authorMurrugarra, Daviden
dc.contributor.authorVeliz-Cuba, Alanen
dc.contributor.authorAguilar, Borisen
dc.contributor.authorArat, Sedaen
dc.contributor.authorLaubenbacher, Reinhard C.en
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentMathematicsen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.date.accessioned2012-08-15T14:59:03Zen
dc.date.available2012-08-15T14:59:03Zen
dc.date.issued2012-06-06en
dc.date.updated2012-08-15T14:59:03Zen
dc.description.abstractModeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationEURASIP Journal on Bioinformatics and Systems Biology. 2012 Jun 06;2012(1):5en
dc.identifier.doihttps://doi.org/10.1186/1687-4153-2012-5en
dc.identifier.urihttp://hdl.handle.net/10919/18769en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderDavid Murrugarra et al.; licensee BioMed Central Ltd.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleModeling stochasticity and variability in gene regulatory networksen
dc.title.serialEURASIP Journal on Bioinformatics and Systems Biologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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