Abstract
Motivation: The analysis and mechanistic modelling of time series gene expression data provided by techniques such as microarrays, NanoString, reverse transcription–polymerase chain reaction and advanced sequencing are invaluable for developing an understanding of the variation in key biological processes. We address this by proposing the estimation of a flexible dynamic model, which decouples temporal synthesis and degradation of mRNA and, hence, allows for transcriptional activity to switch between different states.Results: The model is flexible enough to capture a variety of observed transcriptional dynamics, including oscillatory behaviour, in a way that is compatible with the demands imposed by the quality, time-resolution and quantity of the data. We show that the timing and number of switch events in transcriptional activity can be estimated alongside individual gene mRNA stability with the help of a Bayesian reversible jump Markov chain Monte Carlo algorithm. To demonstrate the methodology, we focus on modelling the wild-type behaviour of a selection of 200 circadian genes of the model plant Arabidopsis thaliana. The results support the idea that using a mechanistic model to identify transcriptional switch points is likely to strongly contribute to efforts in elucidating and understanding key biological processes, such as transcription and degradation.Contact: B.F.Finkenstadt@Warwick.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
One of the archetypal challenges of systems biology is the task of uncovering the network of interactions between genes and proteins using data such as that coming from high-throughput genome-wide technologies or multi-parameter imaging
The aim of this article is to present a novel approach for identifying timing of transcriptional activity from time series mRNA expression data
The model introduced here consists of a piecewise linear simple ordinary differential equation model (ODE) model of mRNA dynamics, which can be fitted efficiently with a reversible jump Markov chain Monte Carlo (RJMCMC) sampler to estimate genespecific parameters, i.e. mRNA stability and number and times of switches in transcriptional activity
Summary
One of the archetypal challenges of systems biology is the task of uncovering the network of interactions between genes and proteins using data such as that coming from high-throughput genome-wide technologies or multi-parameter imaging. Time series gene expression data from techniques such as NanoString, reverse transcription–polymerase chain reaction, microarrays or advanced sequencing are valuable for addressing such tasks especially if the system can be perturbed in an informative way. Such data can be used to get genome-wide understanding of the variation in key biological processes, such as transcription and degradation. One is concerned with better-understood systems, such as the circadian clock or cell cycle, where relatively sophisticated models exist In these cases, it is of interest to uncover both new connections and deeper details of the regulatory interactions. Almost all examples studying the response dynamics when systems are subjected to perturbations, such as drug dosing (Eisen et al, 1998) or stress (Windram et al, 2012), or where the progression of disease is studied (Calvano et al, 2005) fall into this latter category
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