Clumped-MCEM: Inference for multistep transcriptional processes. (August 2019)

• Record Type: Journal Article
• Authors: Shetty, Keerthi S.; B, Annappa
• Is Part Of: Computational biology and chemistry. Volume 81(2019)
• Page Start: 16
• Page End: 20
• Publication Date: 2019-08
• Subjects: Parameter inference -- Mass action kinetics -- Time-series data -- Multistep promoter model -- Model reduction; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2019.107092
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A model reduction strategy is devised for modeling multistep reactions. Using this model reduction, Clumped-MCEM is created which enables simulation and parameter inference. Clumped-MCEM is applied to time-series data of endogenous mouse glutaminase promoter to validate the model assumptions and infer the kinetic parameters. The efficiency of Clumped-MCEM is compared with state-of-the-art methods Bursty MCEM 2 and delay Bursty MCEM. Clumped-MCEM reduces computational cost by 57.58% when compared with Bursty MCEM 2 and 32.19% when compared with delay Bursty MCEM. 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 MCEM 2 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 MCEM 2 and delay Bursty MCEM in less time. Clumped-MCEM reduces computational cost by 57.58% when compared with Bursty MCEM 2 and 32.19% when compared with delay Bursty MCEM.