AUTHOR=Correia Rion B. , Gates Alexander J. , Wang Xuan , Rocha Luis M. 

TITLE=CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks

JOURNAL=Frontiers in Physiology

VOLUME=9

YEAR=2018

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2018.01046

DOI=10.3389/fphys.2018.01046

ISSN=1664-042X

ABSTRACT=<p>Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, <xref ref-type="bibr" rid="B31">2013</xref>). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, <xref ref-type="bibr" rid="B17">2016</xref>; Gates et al., <xref ref-type="bibr" rid="B16">2016</xref>; Manicka, <xref ref-type="bibr" rid="B30">2017</xref>). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their <italic>effective graph</italic> and <italic>dynamics canalizing map</italic>, as well as other tools to uncover minimum sets of control variables.</p>