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Computer Science > Machine Learning

arXiv:1308.5338 (cs)
[Submitted on 24 Aug 2013]

Title:A stochastic hybrid model of a biological filter

Authors:Andrea Ocone (School of Informatics, University of Edinburgh), Guido Sanguinetti (School of Informatics, University of Edinburgh)
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Abstract: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.
Comments: In Proceedings HAS 2013, arXiv:1308.4904
Subjects: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Molecular Networks (q-bio.MN)
Cite as: arXiv:1308.5338 [cs.LG]
  (or arXiv:1308.5338v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1308.5338
Journal reference: EPTCS 124, 2013, pp. 100-108
Related DOI: https://doi.org/10.4204/EPTCS.124.10

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Sat, 24 Aug 2013 14:34:38 UTC (253 KB)
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