Reconstruction of transcriptional dynamics from gene reporter data using differential equations

被引:46
作者
Finkenstadt, Barbel [1 ]
Heron, Elizabeth A. [1 ,2 ]
Komorowski, Michal [1 ,2 ]
Edwards, Kieron [3 ]
Tang, Sanyi [2 ]
Harper, Claire V. [4 ]
Davis, Julian R. E. [5 ]
White, Michael R. H. [4 ]
Millar, Andrew J. [3 ]
Rand, David A. [2 ]
机构
[1] Univ Warwick, Dept Stat, Coventry CV4 7AL, W Midlands, England
[2] Univ Warwick, Syst Biol Ctr, Coventry CV4 7AL, W Midlands, England
[3] Univ Edinburgh, Inst Mol Plant Sci, Edinburgh EH9 3JH, Midlothian, Scotland
[4] Univ Liverpool, Dept Biol, Liverpool L69 3BX, Merseyside, England
[5] Univ Manchester, Sch Med, Manchester M13 9PL, Lancs, England
基金
英国生物技术与生命科学研究理事会; 英国工程与自然科学研究理事会; 英国惠康基金;
关键词
D O I
10.1093/bioinformatics/btn562
中图分类号
Q5 [生物化学];
学科分类号
071010 [生物化学与分子生物学]; 081704 [应用化学];
摘要
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.
引用
收藏
页码:2901 / 2907
页数:7
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