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Bioinformatics Advance Access originally published online on October 30, 2008
Bioinformatics 2008 24(24):2901-2907; doi:10.1093/bioinformatics/btn562
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reconstruction of transcriptional dynamics from gene reporter data using differential equations

Bärbel Finkenstädt 1,*, Elizabeth A. Heron 1,2, Michal Komorowski 1,2, Kieron Edwards 3, Sanyi Tang 2,{dagger}, Claire V. Harper 4, Julian R. E. Davis 5, Michael R. H. White 4, Andrew J. Millar 3 and David A. Rand 2

1Department of Statistics, University of Warwick, Coventry, CV4 7AL, 2Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, 3Institute for Molecular Plant Sciences, University of Edinburgh, Edinburgh EH9 3JH, 4Department of Biology, University of Liverpool and 5School of Medicine, University of Manchester, Liverpool, Manchester

*To whom correspondence should be addressed.


  Abstract

Motivation: Promoter-driven reporter genes, notably luciferase and green fluorescent protein, 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 differential equations (SDEs) and ordinary differential equations (ODEs) 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 SDEs ODEs 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.

Contact: b.f.finkenstadt{at}warwick.ac.uk

Supplementary Information: Supplementary data are available at Bioinformatics online.

{dagger}Present address: College of Mathematical and Information Science, Shaanxi Normal University, Xi'an 710062, P.R. China.

Associate Editor: Jonathan Wren

Received on July 24, 2008; revised on October 1, 2008; accepted on October 25, 2008

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