Statistical inference of regulatory networks for circadian regulation

Aderhold Andrej, Husmeier Dirk () and Grzegorczyk Marco
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Aderhold Andrej: School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QW, UK
Husmeier Dirk: School of Biology, Sir Harold Mitchell Building, University of St Andrews, St Andrews, Fife KY16 9TH, UK
Grzegorczyk Marco: Johann Bernoulli Institute (JBI), Groningen University, Nijenborgh 9, 9747 AG Groningen, The Netherlands

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 3, 227-273

Abstract: We assess the accuracy of various state-of-the-art statistics and machine learning methods for reconstructing gene and protein regulatory networks in the context of circadian regulation. Our study draws on the increasing availability of gene expression and protein concentration time series for key circadian clock components in Arabidopsis thaliana. In addition, gene expression and protein concentration time series are simulated from a recently published regulatory network of the circadian clock in A. thaliana, in which protein and gene interactions are described by a Markov jump process based on Michaelis-Menten kinetics. We closely follow recent experimental protocols, including the entrainment of seedlings to different light-dark cycles and the knock-out of various key regulatory genes. Our study provides relative network reconstruction accuracy scores for a critical comparative performance evaluation, and sheds light on a series of highly relevant questions: it quantifies the influence of systematically missing values related to unknown protein concentrations and mRNA transcription rates, it investigates the dependence of the performance on the network topology and the degree of recurrency, it provides deeper insight into when and why non-linear methods fail to outperform linear ones, it offers improved guidelines on parameter settings in different inference procedures, and it suggests new hypotheses about the structure of the central circadian gene regulatory network in A. thaliana.

Keywords: regulatory network inference; circadian clock; hierarchical Bayesian models; comparative method evaluation; ANOVA (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2013-0051

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