Canalization and Symmetry in Boolean Models for Genetic Regulatory Networks

Abstract

Canalization of genetic regulatory networks has been argued to be favored by evolutionary processes due to the stability that it can confer to phenotype expression. We explore whether a significant amount of canalization and partial canalization can arise in purely random networks in the absence of evolutionary pressures. We use a mapping of the Boolean functions in the Kauffman N-K model for genetic regulatory networks onto a k-dimensional Ising hypercube to show that the functions can be divided into different classes strictly due to geometrical constraints. The classes can be counted and their properties determined using results from group theory and isomer chemistry. We demonstrate that partially canalized functions completely dominate all possible Boolean functions, particularly for higher k. This indicates that partial canalization is extremely common, even in randomly chosen networks, and has implications for how much information can be obtained in experiments on native state genetic regulatory networks.

14 pages, 4 figures; version to appear in J. Phys. A

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Cited by in corpus (7)

• A meta-analysis of Boolean network models reveals design principles of gene regulatory networks
• Canalization and control in automata networks: body segmentation in Drosophila melanogaster
• CANA: A python package for quantifying control and canalization in Boolean Networks
• Self-organization of heterogeneous topology and symmetry breaking in networks with adaptive thresholds and rewiring
• Symmetry in Critical Random Boolean Network Dynamics
• On Analysis and Generation of some Biologically Important Boolean Functions
• Canalization in the Critical States of Highly Connected Networks of Competing Boolean Nodes