Faculty Publications

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    Partitioning studies of glutaminase in polyethylene glycol and salt-based aqueous two-phase systems
    (2013) Bolar, S.; Belur, P.D.; Iyyaswami, I.
    The partitioning behavior of glutaminase produced from Zygosaccharomyces rouxii in polyethylene glycol (PEG)-salt aqueous two-phase systems (ATPSs) was investigated. ATPSs comprising of different PEG-salts were considered. Binodal data and tie lines generated for the selected systems were analyzed and correlated with Othmer-Tobias and Bancroft equations. Effects of salt type, PEG molecular weight, concentrations of phase components, and tie line length on enzyme partitioning were evaluated. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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    Purification of Glutaminase from Zygosaccharomyces rouxii in Polyethylene Glycol– Sodium Sulphate Aqueous Two-Phase System
    (Taylor and Francis Inc. 325 Chestnut St, Suite 800 Philadelphia PA 19106, 2015) Bolar, S.; Iyyaswami, R.; Belur, P.D.
    L-glutaminase (EC 3.5.1.2) produced from Zygosaccharomyces rouxii NRRL-Y 2547 was partitioned in an aqueous two phase system comprising PEG 2000 and sodium sulphate. The effects of tie line length (TLL), pH, broth loading (BL), volume ratio, and neutral salt concentration on enzyme partitioning and purification were investigated. The optimal condition for the partitioning of glutaminase was obtained through response surface methodology and obtained the partition coefficient and yield of 12.99 and 95.12%, respectively. The purification factor of 5.59 and selectivity of 6.52 were achieved at the optimal condition. © © Taylor & Francis Group, LLC.
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    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.
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    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