direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

List of Publications

Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling
Citation key Stimberg:2011:BIM
Author Florian Stimberg and Andreas Ruttor and Manfred Opper
Title of Book Proceedings of the 8th International Workshop on Computational Systems Biology (WCSB)
Pages 169–172
Year 2011
ISBN 978-952-15-2591-9, 978-952-15-2592-6
Address Zürich, Switzerland
Editor Heinz Koeppl and Jugoslava Aćimović and Juha Kesseli and Tuomo Mäki-Marttunen and Antti Larjo and Olli Yli-Harja
Publisher Tampere University of Technology, Tampere, Finland
Series TICSP series \# 57
Abstract 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 to publication Download Bibtex entry

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions