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Oxford University Press (OUP), Bioinformatics, 24(24), p. 2901-2907

DOI: 10.1093/bioinformatics/btn562

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Reconstruction of transcriptional dynamics from gene reporter data using differential equations

Journal article published in 2008 by Bärbel Heron EA Komorowski M. Edwards K. Tang S. Harper CV Millar AJ Davis JRE White MRH and Rand D.-A. Finkenstadt, Elizabeth A. Heron, Michal Komorowski, Kieron Edwards, Sanyi Tang, Claire V. Harper, Julian R. E. Davis, Michael R. H. White, Andrew J. (Andrew J.) Millar, Bärbel Heron E. Komorowski M. Edwards K. Tang S. Harper C. Davis JRE White M. Millar A. Rand D. Finkenstädt, David A. (David A.) Rand ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

MOTIVATION: Promoter driven reporter genes, notably luciferase (luc) and green fluorescent protein (gfp), provide a tool for the generation of a vast array of time-course data sets from living cells and organisms. The aim of this study is to introduce a modeling framework based on stochastic and ordinary differential equations that addresses the problem of reconstructing transcription time course profiles and associated degradation rates. The dynamical model is embedded into a Bayesian framework and inference is performed using Markov chain Monte Carlo algorithms. RESULTS: We present three case studies where the methodology is used to reconstruct unobserved transcription profiles and to estimate associated degradation rates. We discuss advantages and limits of fitting either stochastic or ordinary differential equations and address the problem of parameter identifiability when model variables are unobserved. We also suggest functional forms such as on/off switches and stimulus response functions to model transcriptional dynamics and present results of fitting these to experimental data. SUPPLEMENTARY INFORMATION: Supplementary information (SI) is provided with the submission. CONTACT: B.F.Finkenstadt@Warwick.ac.uk.