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Identification of gene regulation models from single-cell data

dc.contributor.authorWeber, Lisa, author
dc.contributor.authorRaymond, Will, author
dc.contributor.authorMunsky, Brian, author
dc.date.accessioned2017-11-13T20:49:23Z
dc.date.available2017-11-13T20:49:23Z
dc.date.issued2017
dc.description.abstractIn quantitative biology, one may use many different model scales or approaches to match models to experimental data. We use a simplified gene regulation model with a time-dependent input signal to illustrate many concepts, including ODE analyses of deterministic processes; chemical master equation and finite-state projection analyses of heterogeneous processes; and stochastic simulations. We consider several model hypotheses and simulated single-cell data to illustrate mechanism and parameter identification as precisely as possible, while exploring how approach or experiment design affect parameter uncertainty. Our approach is based upon previous investigations to explore signal-activated gene expression models in yeast and human cells.en_US
dc.format.mediumborn digital
dc.format.mediumStudent works
dc.format.mediumposters
dc.identifier.urihttps://hdl.handle.net/10217/184929
dc.languageEnglishen_US
dc.language.isoengen_US
dc.publisherColorado State University. Librariesen_US
dc.relation.ispartof2017 Projects
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectgene regulation
dc.subjectparameter identification
dc.subjectfinite state projection
dc.subjectpredictive modeling
dc.subjectparameter uncertainty
dc.titleIdentification of gene regulation models from single-cell dataen_US
dc.title.alternative309 - Lisa McBride Weberen_US
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