Modelling Biological Clocks with Bio-PEPA: Stochasticity and Robustness for the Neurospora crassa Circadian Network

  • Authors:

  • Ozgur E. Akman;Federica Ciocchetta;Andrea Degasperi;Maria Luisa Guerriero

  • Affiliations:

  • Centre for Systems Biology at Edinburgh, The University of Edinburgh, Edinburgh, Scotland, UK EH9 3JU;The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Trento, Italy;Laboratory for Foundations of Computer Science, The University of Edinburgh, Edinburgh, Scotland, UK EH8 9AB;Department of Computing Science, The University of Glasgow, Glasgow, Scotland, UK G12 8QQ

  • Venue:
  • CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
  • Year:
  • 2009

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Abstract

Circadian clocks are biochemical networks, present in nearly all living organisms, whose function is to regulate the expression of specific mRNAs and proteins to synchronise rhythms of metabolism, physiology and behaviour to the 24 hour day/night cycle. Because of their experimental tractability and biological significance, circadian clocks have been the subject of a number of computational modelling studies. In this study we focus on the simple circadian clock of the fungus Neurospora crassa . We use the Bio-PEPA process algebra to develop both a stochastic and a deterministic model of the system. The light on/off mechanism responsible for entrainment to the day/night cycle is expressed using discrete time-dependent events in Bio-PEPA. In order to validate our model, we compare it against the results of previous work which demonstrated that the deterministic model is in agreement with experimental data. Here we investigate the effect of stochasticity on the robustness of the clock's function in biological timing. In particular, we focus on the variations in the phase and amplitude of oscillations in circadian proteins with respect to different factors such as the presence/absence of a positive feedback loop, and the presence/absence of light. The time-dependent sensitivity of the model with respect to some key kinetic parameters is also investigated.