Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

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dc.citation.articleNumber204108en_US
dc.citation.issueNumber20en_US
dc.citation.journalTitleThe Journal of Chemical Physicsen_US
dc.citation.volumeNumber140en_US
dc.contributor.authorGupta, Chinmayaen_US
dc.contributor.authorLópez, José Manuelen_US
dc.contributor.authorAzencott, Roberten_US
dc.contributor.authorBennett, Matthew R.en_US
dc.contributor.authorJosić, Krešimiren_US
dc.contributor.authorOtt, Williamen_US
dc.contributor.orgInstitute of Biosciences and Bioengineeringen_US
dc.date.accessioned2017-05-24T16:33:36Zen_US
dc.date.available2017-05-24T16:33:36Zen_US
dc.date.issued2014en_US
dc.description.abstractDelay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.en_US
dc.identifier.citationGupta, Chinmaya, López, José Manuel, Azencott, Robert, et al.. "Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations." <i>The Journal of Chemical Physics,</i> 140, no. 20 (2014) AIP Publishing: http://dx.doi.org/10.1063/1.4878662.en_US
dc.identifier.doihttp://dx.doi.org/10.1063/1.4878662en_US
dc.identifier.urihttps://hdl.handle.net/1911/94379en_US
dc.language.isoengen_US
dc.publisherAIP Publishingen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.titleModeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equationsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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