Statistical inference of regulatory networks for circadian regulation

Aderhold, A., Husmeier, D. and Grzegorczyk, M. (2014) Statistical inference of regulatory networks for circadian regulation. Statistical Applications in Genetics and Molecular Biology, 13(3), pp. 227-273. (doi:10.1515/sagmb-2013-0051)

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Publisher's URL: http://dx.doi.org/10.1515/sagmb-2013-0051

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Aderhold, A., Husmeier, D., and Grzegorczyk, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistical Applications in Genetics and Molecular Biology
Publisher:De Gruyter
ISSN:2194-6302
ISSN (Online):1544-6115
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Statistical Applications in Genetics and Molecular Biology 13(3):227-273
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
66883UNSPECIFIEDTimetDirk HusmeierEU - FP7UNSPECIFIEDUNSPECIFIED