Modeling stochasticity and robustness in gene regulatory networks

Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.

Published in:
Proceedings of the ISMB/ECCB 2009 Conference, 25, I101-I109
Presented at:
Joint Conference of Intelligent Systems for Molecular Biology (ISMB) and 8th European Conference on Computational Biology (ECCB), Stockholm, Sweden, June 27-July 02, 2009

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 Record created 2010-11-30, last modified 2019-12-05

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