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dc.contributor.authorShetty, K.S.
dc.contributor.authorB, A.
dc.date.accessioned2020-03-31T08:18:45Z-
dc.date.available2020-03-31T08:18:45Z-
dc.date.issued2019
dc.identifier.citationComputational Biology and Chemistry, 2019, Vol.81, , pp.16-20en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/10225-
dc.description.abstractMany biochemical events involve multistep reactions. Among them, an important biological process that involves multistep reaction is the transcriptional process. A widely used approach for simplifying multistep reactions is the delayed reaction method. In this work, we devise a model reduction strategy that represents several OFF states by a single state, accompanied by specifying a time delay for burst frequency. Using this model reduction, we develop Clumped-MCEM which enables simulation and parameter inference. We apply this method to time-series data of endogenous mouse glutaminase promoter, to validate the model assumptions and infer the kinetic parameters. Further, we compare efficiency of Clumped-MCEM with state-of-the-art methods Bursty MCEM2 and delay Bursty MCEM. Simulation results show that Clumped-MCEM inference is more efficient for time-series data and is able to produce similar numerical accuracy as state-of-the-art methods Bursty MCEM2 and delay Bursty MCEM in less time. Clumped-MCEM reduces computational cost by 57.58% when compared with Bursty MCEM2 and 32.19% when compared with delay Bursty MCEM. 2019 Elsevier Ltden_US
dc.titleClumped-MCEM: Inference for multistep transcriptional processesen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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