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

Frontiers in Physiology
Rion B CorreiaLuis M Rocha

Abstract

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, 2013). 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, 2016; Gates et al., 2016; Manicka, 2017). 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 effective graph and dynamics canalizing map, as well as other tools to uncover...Continue Reading

References

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Citations

Jan 11, 2020·Bulletin of Mathematical Biology·Luis Sordo VieiraDavid Murrugarra
Oct 13, 2018·Physical Review Letters·Bryan C DanielsSara I Walker
Aug 28, 2020·Bioinformatics·Cui Su, Jun Pang
Mar 20, 2021·Proceedings of the National Academy of Sciences of the United States of America·Alexander J GatesLuis M Rocha
Mar 27, 2021·Proceedings of the National Academy of Sciences of the United States of America·Kishore HariMohit Kumar Jolly
Jan 20, 2022·Journal of the Royal Society, Interface·Santosh ManickaLuis M Rocha

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Software Mentioned

CellNetOptimizer
CANA
Cytoscape
Espresso
Cell Collective
PANET
BooleanNet
BooleanNetwork
Graphviz
Dynamic Deterministic Effects Propagation Networks ( DDEPN ) p...

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