A stochastic hybrid model of a biological filter

Andrea Ocone
(School of Informatics, University of Edinburgh)
Guido Sanguinetti
(School of Informatics, University of Edinburgh)

We present a hybrid model of a biological filter, a genetic circuit which removes fast fluctuations in the cell's internal representation of the extra cellular environment. The model takes the classic feed-forward loop (FFL) motif and represents it as a network of continuous protein concentrations and binary, unobserved gene promoter states. We address the problem of statistical inference and parameter learning for this class of models from partial, discrete time observations. We show that the hybrid representation leads to an efficient algorithm for approximate statistical inference in this circuit, and show its effectiveness on a simulated data set.

In Luca Bortolussi, Manuela L. Bujorianu and Giordano Pola: Proceedings Third International Workshop on Hybrid Autonomous Systems (HAS 2013), Rome, 17th March 2013, Electronic Proceedings in Theoretical Computer Science 124, pp. 100–108.
Published: 22nd August 2013.

ArXived at: http://dx.doi.org/10.4204/EPTCS.124.10 bibtex PDF
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