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

Article English OPEN
Grzegorczyk, Marco ; Aderhold, Andrej ; Husmeier, Dirk (2015)

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.
  • References (16)
    16 references, page 1 of 2

    Aderhold, A., D. Husmeier, and M. Grzegorczyk (2014): \Statistical inference of regulatory networks for circadian regulation," Statistical Applications in Genetics and Molecular Biology (SAGMB), 13, 227{273.

    Ahmed, A. and E. P. Xing (2009): \Recovering time-varying networks of dependencies in social and biological studies," Proceedings of the National Academy of Sciences, 106, 11878{11883.

    Aijo, T. and H. Lahdesmaki (2009): \Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics," Bioinformatics, 25, 2937{2944.

    Barenco, M., D. Tomescu, D. Brewer, R. Callard, J. Stark, and M. Hubank (2006): \Ranked prediction of p53 targets using hidden variable dynamic modeling," Genome Biology, 7, R25.

    Beal, M. (2003): Variational Algorithms for Approximate Bayesian Inference, Ph.D. thesis, Gatsby Computational Neuroscience Unit, University College London, UK.

    Beal, M., F. Falciani, Z. Ghahramani, C. Rangel, and D. Wild (2005): \A Bayesian approach to reconstructing genetic regulatory networks with hidden factors," Bioinformatics, 21, 349{356.

    Blasing, O. E., Y. Gibon, M. Gunther, M. Hohne, R. Morcuende, D. Osuna, O. Thimm, B. Usadel, W.-R. Scheible, and M. Stitt (2005): \Sugars and circadian regulation make major contributions to the global regulation of diurnal gene expression in arabidopsis," The Plant Cell Online, 17, 3257{3281.

    Brandt, S. (1999): Data Analysis: Statistical and Computational Methods for Scientists and Engineers, New York, USA: Springer.

    Chevaleyre, Y., U. Endriss, J. Lang, and N. Maudet (2007): A short introduction to computational social choice, Springer.

    Chib, S. and I. Jeliazkov (2001): \Marginal likelihood from the Metropolis-Hastings output," Journal of the American Statistical Association, 96, 270{281.

  • Metrics
    No metrics available
Share - Bookmark