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dc.contributor.authorGupta, Ankit
dc.date.accessioned2017-06-08T06:32:30Z
dc.date.available2017-06-08T06:32:30Z
dc.date.issued2016-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624858
dc.description.abstractQuantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/8d696996840f415cbdac143ca3c207021d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleEstimation of parameter sensitivities for stochastic reaction networks
dc.typePresentation
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
dc.contributor.institutionETH Zurich
refterms.dateFOA2018-06-13T14:47:08Z


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