University of Cambridge > > CoSBi Computational and Systems Biology Series > Analyzing the effect of noise on various models of Circadian Clock and Cell Cycle coupling

Analyzing the effect of noise on various models of Circadian Clock and Cell Cycle coupling

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If you have a question about this talk, please contact Dr Fabien Petitcolas.

Abstract: The daily rhythm can influence the proliferation rate of many cell types. In the mammalian system the transcription of the cell cycle regulatory protein Wee1 is controlled by the circadian clock. Computational modeling of this coupling of the circadian and cell cycle oscillators can lead to multimodal cell cycle time distributions. Here we further extend and analyze this model in a stochastic framework. We build models at different levels of abstraction in BlenX and compare the statistics of their stochastic simulation results with a newly introduced method based on Fourier analysis. The original model is compared with an extension by inferring parameter rates from experiments on circadian rhythm and a process calculi based unpacking of phosphorylation and complexation reactions. Our results reveal the similarities and differences between the various levels of abstractions and suggest further directions for future experimental and computational investigations.

Biography: Alessandro Romanel attended the Faculty of Science at the University of Trento (Italy), where he obtained a Bachelor’s (2004) and Master’s (2006) degree in Computer Science. In 2004 he worked as a postgraduate researcher at CREATE -NET International Research Center. From September until December 2008, Alessandro was a visiting student at the Computer and Information Science department at the University of Pennsylvania, and was supervised by Professor Benjamin C. Pierce. Currently, he is a third-year PhD student at the ICT International Doctorate School at the University of Trento (Italy), carrying out research activities at CoSBi and supervised by Professor Corrado Priami.

This talk is part of the CoSBi Computational and Systems Biology Series series.

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