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DC Field | Value | Language |
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dc.contributor.author | Shetty, K.S. | |
dc.contributor.author | B, A. | |
dc.date.accessioned | 2020-03-31T08:18:45Z | - |
dc.date.available | 2020-03-31T08:18:45Z | - |
dc.date.issued | 2019 | |
dc.identifier.citation | Computational Biology and Chemistry, 2019, Vol.81, , pp.16-20 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/10225 | - |
dc.description.abstract | Many 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 Ltd | en_US |
dc.title | Clumped-MCEM: Inference for multistep transcriptional processes | en_US |
dc.type | Article | en_US |
Appears in Collections: | 1. Journal Articles |
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