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Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2011,

Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2011, Article ID 572876, 5 pages doi:10.1155/2011/572876 Research Article Inference of Kinetic Parameters of Delayed Stochastic Models of Gene Expression Using a Markov Chain Approximation Henrik Mannerstrom,1 Olli Yli-Harja,1, 2 and Andre S. Ribeiro1 1 Computational Systems Biology Research Group, Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland 2 Institute for Systems Biology, Seattle, WA 98103, USA Correspondence should be addressed to Henrik Mannerstrom, henrik.mannerstrom@tut.fi Received 21 October 2010; Accepted 4 December 2010 Academic Editor: Carsten Wiuf Copyright © 2011 Henrik Mannerstrom et al. This is an. | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2011 Article ID 572876 5 pages doi 10.1155 2011 572876 Research Article Inference of Kinetic Parameters of Delayed Stochastic Models of Gene Expression Using a Markov Chain Approximation Henrik Mannerstrom 1 Olli Yli-Harja 1 2 and Andre S. Ribeiro1 1 Computational Systems Biology Research Group Department of Signal Processing Tampere University of Technology P.O. Box 553 33101 Tampere Finland 2 Institute for Systems Biology Seattle WA 98103 USA Correspondence should be addressed to Henrik Mannerstrom henrik.mannerstrom@tut.fi Received 21 October 2010 Accepted 4 December 2010 Academic Editor Carsten Wiuf Copyright 2011 Henrik Mannerstrom et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We propose a Markov chain approximation of the delayed stochastic simulation algorithm to infer properties of the mechanisms in prokaryote transcription from the dynamics of RNA levels. We model transcription using the delayed stochastic modelling strategy and realistic parameter values for rate of transcription initiation and RNA degradation. From the model we generate time series of RNA levels at the single molecule level from which we use the method to infer the duration of the promoter open complex formation. This is found to be possible even when adding external Gaussian noise to the RNA levels. 1. Introduction Gene expression dynamics is influenced by even small fluctuations on the levels of various molecular species such as RNA polymerases and transcription factors. In some cases even the presence of a single molecule can cause phenotypic switching 1 . This makes the cellular metabolism inherently stochastic 2 . The stochasticity in the abundance of a substance is in general thought of being noise that obscures a signal .