Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana

Authors:
Rónán Daly;Kieron D. Edwards;John S. O'Neill;Stuart Aitken;Andrew J. Millar;Mark Girolami
Affiliations:
Inference Group, Department of Computing Science, University of Glasgow,;Advanced Technologies (Cambridge) Limited,;Institute of Metabolic Science, Metabolic Research Laboratories, University of Cambridge,;Centre for Systems Biology at Edinburgh, The University of Edinburgh,;Centre for Systems Biology at Edinburgh, The University of Edinburgh,;Inference Group, Department of Computing Science, University of Glasgow,
Venue:
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2009

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Abstract

Modelling gene regulatory networks in organisms is an important task that has recently become possible due to large scale assays using technologies such as microarrays. In this paper, the circadian clock of Arabidopsis thaliana is modelled by fitting dynamic Bayesian networks to luminescence data gathered from experiments. This work differs from previous modelling attempts by using higher-order dynamic Bayesian networks to explicitly model the time lag between the various genes being expressed. In order to achieve this goal, new techniques in preprocessing the data and in evaluating a learned model are proposed. It is shown that it is possible, to some extent, to model these time delays using a higher-order dynamic Bayesian network.