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Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling
Zitatschlüssel Stimberg:2011:BIM
Autor Florian Stimberg and Andreas Ruttor and Manfred Opper
Buchtitel Proceedings of the 8th International Workshop on Computational Systems Biology (WCSB)
Seiten 169–172
Jahr 2011
ISBN 978-952-15-2591-9, 978-952-15-2592-6
Adresse Zürich, Switzerland
Herausgeber Heinz Koeppl and Jugoslava Aćimović and Juha Kesseli and Tuomo Mäki-Marttunen and Antti Larjo and Olli Yli-Harja
Verlag Tampere University of Technology, Tampere, Finland
Serie TICSP series # 57
Zusammenfassung The activity of transcription factors is often difficult to measure directly in micro-array experiments. An alternative approach is to infer these quantities from noisy expression data of the target genes. In this paper we present a Markov chain Monte Carlo sampler suitable for this task. The algorithm uses the full time series of available observations, so that the dynamics of the system state as well as transcriptional parameters can be estimated. Samples are generated either directly from conditional probability distributions or by means of Metropolis-Hastings steps. We test our method on toy data sets of different sizes and compare the result for a real data set of the yeast metabolic cycle with an existing approximation.
Link zur Publikation [1] Download Bibtex Eintrag [2]
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