Finkenstädt, Bärbel, Heron, Elizabeth A., Komorowski, Michal, Edwards, Kieron, Tang, Sanyi, Harper, Claire V., Davis, Julian R. E., White, Michael R. H., Millar, A. J. (Andrew J.) and Rand, D. A. (David A.). (2008) Reconstruction of transcriptional dynamics from gene reporter data using differential equations. Bioinformatics, Vol.24 (No.24). pp. 2901-2907. ISSN 1367-4803

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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.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics Q Science > QP Physiology
Divisions:	Faculty of Science > Statistics Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH):	Reporter genes, Transcription factors, Differential equations
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Date:	15 December 2008
Volume:	Vol.24
Number:	No.24
Number of Pages:	7
Page Range:	pp. 2901-2907
Identification Number:	10.1093/bioinformatics/btn562
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Engineering and Physical Sciences Research Council (EPSRC), European Union (EU), Centre for Integrative and Systems Biology (CSBE), University of Warwick. Department of Statistics, Wellcome Trust (London, England), The Prof. John Glover Memorial Postdoctoral Fellowship
Grant number:	E015263 (BBSRC), GR/S29256/01 (BBSRC/EPSRC), EP/C544587/1 (EPSRC), 005137 (EU), 067252 (WT)
References:	Aase,S.O. and Ruoff,P. (2008) Semi-algebraic optimization of temperature compensation in a general switch-type negative feedback model of circadian clocks. J. Math. Biol., 56, 279–292. Durham,G.B. and Gallant,A.R. (2002) Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes. J. Bus. Econ. Stat., 20, 297–316. Elerian,O. et al. (2001) Likelihood inference for discretely observed non-linear diffusions. Econometrica, 69, 959–993. Gamerman,D. and Lopes,H.F. (2006) Markov Chain Monte Carlo Stochastic Simulation for Bayesian Inference, 2nd edn. Chapman & Hall/CRC, Boca Raton, London, New York. Gillespie,D.T. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem., 81, 2340–2361. Gillespie,D.T. (1992) A rigorous derivation of the chemical master equation. Physica A, 188, 404–425. Goldbeter,A. (2002) Computational approaches to cellular rhythms. Nature, 420, 238–245. Hengl,S. et al. (2007) Data-based identifiability analysis of non-linear dynamical models. Bioinformatics, 23, 2612–2618. Heron,E.A. et al. (2007) Bayesian inference for dynamic transcriptional regulation; the hes1 system as a case study. Bioinformatics, 23, 2589–2595. Jensen,M.H. et al. (2003) Sustained oscillations and time delays in gene expression of protein Hes1. Febs Lett., 541, 176–177. Kalir,S. and Alon,U. (2004) Using a quantitative blueprint to reprogram the dynamics of the flagella gene network. Cell, 117, 713–720. Kloeden,P.E. and Platen,E. (1999) Numerical Solution of Stochastic Differential Equations. 3rd edn. Springer-Verlag, Berlin, New York, 1999. Locke,J.C.W. et al. (2005a) Modelling genetic networks with noisy and varied experimental data: the circadian clock in Arabidopsis thaliana. J. Theor. Biol., 234, 383–393. Locke,J.C.W. et al. (2005b) Extension of a genetic network model by iterative experimentation and mathematical analysis. Mol. Syst. Biol., 1, E1–E9. Millar,A.J. et al. (1995) Circadian clock mutants in Arabidopsis identified by luciferase imaging. Science, 267, 1161–1163. Millar,A.J. and Kay,S.A. (1996) Integration of circadian and phototransduction pathways in the network controlling cab gene transcription in Arabidopsis. Proc. Natl Acad. Sci. USA, 93, 15491–15496. Nelson,D.E. et al. (2004) Oscillations in NF-kappa B signaling control the dynamics of gene expression. Science, 306, 704–708. Ronen,M. et al. (2002) Assigning numbers to the arrows: parameterizing a gene regulatory network by using accurate expression kinetics. Proc. Natl Acad. Sci. USA, 99, 10555–10560. Tanner,M.A. and Wong,W.H. (1987) The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. USA, 82, 528–540. Wang,Z. and Tobin,E.M. (1998) Constitutive expression of the circadian clock associated 1 (cca1) gene disrupts circadian rhythms and suppresses its own expression. Cell, 93, 1207–1217. Yakir,E. et al. (2007) Circadian clock associated1 transcript stability and the entrainment of the circadian clock in arabidopsis. Plant Physiol., 145, 925–932.
URI:	http://wrap.warwick.ac.uk/id/eprint/28949

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