Reconstruction of transcriptional dynamics from gene reporter data using differential equations.
BACKGROUND
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.
Finkenstädt
Bärbel
B
Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk
Heron
Elizabeth A
EA
Komorowski
Michal
M
Edwards
Kieron
K
Tang
Sanyi
S
Harper
Claire V
CV
Davis
Julian R E
JR
White
Michael R H
MR
Millar
Andrew J
AJ
Rand
David A
DA
D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D003198:Computer Simulation; D017343:Genes, Plant; D017930:Genes, Reporter / Q000235:genetics*; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D051381:Rats; D014158:Transcription, Genetic