Additive functions in boolean models of gene regulatory network modules.

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Serval ID
serval:BIB_46BAD6D21823
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Additive functions in boolean models of gene regulatory network modules.
Journal
Plos One
Author(s)
Darabos C., Di Cunto F., Tomassini M., Moore J.H., Provero P., Giacobini M.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Publication state
Published
Issued date
2011
Volume
6
Number
11
Pages
e25110
Language
english
Notes
Publication types: Journal ArticlePublication Status: ppublish
Abstract
Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
Pubmed
Web of science
Open Access
Yes
Create date
05/01/2012 17:34
Last modification date
20/08/2019 14:52
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