Journal Article

Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources

Xiaofeng Dai, Olli Yli-Harja and Andre S. Ribeiro

in Bioinformatics

Volume 25, issue 18, pages 2362-2368
Published in print September 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp411
Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources

Show Summary Details

Preview

Motivation: Gene regulatory networks (GRNs) are stochastic, thus, do not have attractors, but can remain in confined regions of the state space, i.e. the ‘noisy attractors’, which define the cell type and phenotype.

Results: We propose a gamma-Bernoulli mixture model clustering algorithm (ΓBMM), tailored for quantizing states from gamma and Bernoulli distributed data, to determine the noisy attractors of stochastic GRN. ΓBMM uses multiple data sources, naturally selects the number of states and can be extended to other parametric distributions according to the number and type of data sources available. We apply it to protein and RNA levels, and promoter occupancy state of a toggle switch and show that it can be bistable, tristable or monostable depending on its internal noise level. We show that these results are in agreement with the patterns of differentiation of model cells whose pathway choice is driven by the switch. We further apply ΓBMM to a model of the MeKS module of Bacillus subtilis, and the results match experimental data, demonstrating the usability of ΓBMM.

Availability: Implementation software is available upon request.

Contact: andre.sanchesribeiro@tut.fi; xiaofeng.dai@tut.fi

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5176 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.