CANA: A python package for quantifying control and canalization in  Boolean Networks

Correia, Rion Brattig; Gates, Alexander J.; Wang, Xuan; Rocha, Luis M.

Publication

CANA: A python package for quantifying control and canalization in Boolean Networks

2018-08-14Journal article

dc.contributor.author	Correia, Rion Brattig
dc.contributor.author	Gates, Alexander J.
dc.contributor.author	Wang, Xuan
dc.contributor.author	Rocha, Luis M.
dc.date.accessioned	2018-09-04T14:24:24Z
dc.date.available	2018-09-04T14:24:24Z
dc.date.issued	2018-08-14
dc.description	This deposit is composed by the main article. The supplementary materials can be accessed through the following link: github.com/rionbr/CANA	pt_PT
dc.description.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 minimum sets of control variables.	pt_PT
dc.description.sponsorship	RC was supported by CAPES Foundation grant 18668127, Instituto Gulbenkian de Ciência (IGC), and Indiana University Precision Health to Population Health (P2P) Study. LR was partially funded by the National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01, by a Fulbright Commission fellowship, and by NSF-NRT grant 1735095, Interdisciplinary Training in Complex Networks and Systems. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Correia RB, Gates AJ, Wang X and Rocha LM (2018) CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks. Front. Physiol. 9:1046. doi: 10.3389/fphys.2018.01046	pt_PT
dc.identifier.doi	10.3389/fphys.2018.01046	pt_PT
dc.identifier.uri	http://hdl.handle.net/10400.7/892
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Frontiers Media	pt_PT
dc.relation	18668127	pt_PT
dc.relation	01LM011945-01	pt_PT
dc.relation	1735095	pt_PT
dc.relation.publisherversion	https://www.frontiersin.org/articles/10.3389/fphys.2018.01046/full#h9	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Boolean networks	pt_PT
dc.subject	automata	pt_PT
dc.subject	canalization	pt_PT
dc.subject	python package	pt_PT
dc.subject	biochemical regulation	pt_PT
dc.subject	logical modeling	pt_PT
dc.subject	network dynamics	pt_PT
dc.subject	complex systems	pt_PT
dc.title	CANA: A python package for quantifying control and canalization in Boolean Networks	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	8	pt_PT
oaire.citation.startPage	1	pt_PT
oaire.citation.title	Frontiers in Physiology	pt_PT
oaire.citation.volume	9	pt_PT
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT