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

  • Rónán Daly
  • Kieron D. Edwards
  • John S. O’Neill
  • Stuart Aitken
  • Andrew J. Millar
  • Mark Girolami
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)


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.


Dynamic Bayesian Network Gene Regulatory Network Gene Expression Arabidopsis Thaliana 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rónán Daly
    • 1
  • Kieron D. Edwards
    • 2
  • John S. O’Neill
    • 3
  • Stuart Aitken
    • 4
  • Andrew J. Millar
    • 4
  • Mark Girolami
    • 1
  1. 1.Inference Group, Department of Computing ScienceUniversity of GlasgowUK
  2. 2.Advanced Technologies (Cambridge) LimitedUK
  3. 3.Institute of Metabolic Science, Metabolic Research LaboratoriesUniversity of CambridgeUK
  4. 4.Centre for Systems Biology at EdinburghThe University of EdinburghUK

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