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Reconstruction of transcriptional dynamics from gene reporter data using differential equations

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  • Bärbel Finkenstädt
  • Elizabeth A Heron
  • Michal Komorowski
  • Kieron Edwards
  • Sanyi Tang
  • Claire V Harper
  • Julian R E Davis
  • Michael R H White
  • Andrew J Millar
  • David A Rand

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    Rights statement: 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.

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Original languageEnglish
Pages (from-to)2901-7
Number of pages7
JournalBioinformatics
Volume24
Issue number24
DOIs
Publication statusPublished - 2008

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.

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