Faculty Publications
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Item Inferring transcriptional dynamics with time-dependent reaction rates using stochastic simulation(Springer Verlag, 2018) Shetty, K.S.; Annappa, B.Gene transcription is a stochastic process. Single-cell experiments show that transcription occurs in bursty fashion. It has been shown that stochastic switching between promoter active (ON) and inactive (OFF) states results in bursts. However, increasing evidence on promoter switching between ON and OFF states suggests that promoter states’ times can be non-exponential and exhibits multi-OFF mechanism. All these experimental facts motivate to present a multi-state promoter model of gene expression for characterizing promoter architecture. In this paper, we develop a model for promoter with arbitrary number of promoter OFF states and single ON state using Erlang-distributed ON/OFF times. In this paper, we use Monte Carlo approach of Expectation–Maximization (MCEM) that uses direct method of Gillespie’s Stochastic Simulation Algorithm (SSA) to infer the unknown kinetic rates, number of promoter states and parameters representing estimation errors of inferred parameters. We use graphics processing units (GPUs) to accelerate the MCEM. Application of MCEM to time-series data of endogenous mouse glutaminase promoter shows that promoter switching between ON and OFF states effects bursting. The production of mRNAs becomes consistent as the number of OFF states increases and arrives at deterministic time intervals. The main objective is to analyse the impact of promoter switching on bursty production of mRNAs. Our analysis demonstrates that bursting kinetics depends on the promoter ON/OFF states. © Springer Nature Singapore Pte Ltd. 2018.Item Transcriptional processes: Models and inference(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2018) Shetty, K.S.; Annappa, B.Many biochemical events involve multistep reactions. One of the most important biological processes that involve multistep reaction is the transcriptional process. Models for multistep reaction necessarily need multiple states and it is a challenge to compute model parameters that best agree with experimental data. Therefore, the aim of this work is to design a multistep promoter model which accurately characterizes transcriptional bursting and is consistent with observed data. To address this issue, we develop a model for promoters with several OFF states and a single ON state using Erlang distribution. To explore the combined effects of model and data, we combine Monte Carlo extension of Expectation Maximization (MCEM) and delay Stochastic Simulation Algorithm (DSSA) and call the resultant algorithm as delay Bursty MCEM. We apply this algorithm to time-series data of endogenous mouse glutaminase promoter to validate the model assumptions and infer the kinetic parameters. Our results show that with multiple OFF states, we are able to infer and produce a model which is more consistent with experimental data. Our results also show that delay Bursty MCEM inference is more efficient. © 2018 World Scientific Publishing Europe Ltd.Item Clumped-MCEM: Inference for multistep transcriptional processes(Elsevier Ltd, 2019) Shetty, K.S.; B, A.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