Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles

Grzegorczyk Marco (), Aderhold Andrej and Husmeier Dirk
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Grzegorczyk Marco: Johann Bernoulli Institute (JBI), Faculteit Wiskunde en Natuurwetenschappen (FWN), Groningen University, Nijenborgh 9, Postbus 407, 9747 AG Groningen 9700 AK, The Netherlands
Aderhold Andrej: School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QW, UK
Husmeier Dirk: School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QW, UK

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 2, 143-167

Abstract: There has been much interest in reconstructing bi-directional regulatory networks linking the circadian clock to metabolism in plants. A variety of reverse engineering methods from machine learning and computational statistics have been proposed and evaluated. The emphasis of the present paper is on combining models in a model ensemble to boost the network reconstruction accuracy, and to explore various model combination strategies to maximize the improvement. Our results demonstrate that a rich ensemble of predictors outperforms the best individual model, even if the ensemble includes poor predictors with inferior individual reconstruction accuracy. For our application to metabolomic and transcriptomic time series from various mutagenesis plants grown in different light-dark cycles we also show how to determine the optimal time lag between interactions, and we identify significant interactions with a randomization test. Our study predicts new statistically significant interactions between circadian clock genes and metabolites in Arabidopsis thaliana, and thus provides independent statistical evidence that the regulation of metabolism by the circadian clock is not uni-directional, but that there is a statistically significant feedback mechanism aiming from metabolism back to the circadian clock.

Keywords: circadian clock; determination of time delays; model ensembles; plant metabolism; regulatory network inference (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0041

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