Publication details

Dynamic network-based epistasis analysis: Boolean examples

Authors

AZPEITIA Eugenio BENITEZ Mariana PADILLA-LONGORIA Pablo ESPINOSA-SOTO Carlos ALVAREZ-BUYLLA Elena R.

Year of publication 2011
Type Article in Periodical
Magazine / Source Frontiers in Plant Science
MU Faculty or unit

Central European Institute of Technology

Citation
web Article freely available
Doi http://dx.doi.org/10.3389/fpls.2011.00092
Field Genetics and molecular biology
Keywords Boolean network; dynamic model; epistasis
Description In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis.
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