A Stochastic Model of Gene Transcription: an Application to L1 Retrotransposition Events
Overview
Affiliations
A simplified mathematical model of gene transcription is presented based on a system of coupled chemical reactions and a corresponding set of stochastic equations similar to those used in enzyme kinetics theory. The quasi-stationary distribution for the model is derived and its usefulness illustrated with an example of model parameters estimation using sparse time course data on L1 retrotransposon expression kinetics. The issue of model validation is also discussed and a simple validation procedure for the estimated model is devised. The procedure compares model predicted values with the laboratory data via the standard Bayesian techniques with the help of modern Markov-Chain Monte-Carlo methodology.
Martin M, Brown D, Ramos K Comput Struct Biotechnol J. 2021; 19:5667-5677.
PMID: 34765087 PMC: 8554170. DOI: 10.1016/j.csbj.2021.10.003.
Incorporating age and delay into models for biophysical systems.
KhudaBukhsh W, Kang H, Kenah E, Rempala G Phys Biol. 2020; 18(1):015002.
PMID: 33075757 PMC: 9211760. DOI: 10.1088/1478-3975/abc2ab.
Gupta A, Rawlings J AIChE J. 2016; 60(4):1253-1268.
PMID: 27429455 PMC: 4946376. DOI: 10.1002/aic.14409.
Bootstrapping least-squares estimates in biochemical reaction networks.
Linder D, Rempala G J Biol Dyn. 2015; 9:125-46.
PMID: 25898769 PMC: 4408559. DOI: 10.1080/17513758.2015.1033022.
Algebraic methods for inferring biochemical networks: a maximum likelihood approach.
Craciun G, Pantea C, Rempala G Comput Biol Chem. 2009; 33(5):361-7.
PMID: 19709932 PMC: 2753754. DOI: 10.1016/j.compbiolchem.2009.07.014.