Rion B. Correia1,2,3, Alexander J. Gates1,4, Xuan Wang1, Luis M. Rocha1,2,*
1School of Informatics, Computing, and Engineering, Indiana University, Bloomington IN, USA
2Instituto Gulbenkian de Ciencia, Portugal
3CAPES Foundation, Ministry of Education of Brazil
4Center for Complex Networks Research, Northeastern University, Boston, MA, United States
* To whom correspondence she be addressed.
Citation: R.B. Correia, A.J. Gates, X. Wang, L.M. Rocha . "CANA: A python package for quantifying control and canalization in Boolean Networks." Frontiers in Physiology. 9: 1046. DOI: 10.3389/fphys.2018.01046. The arXiv:1803.04774 print is also available.
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.
Keywords: complex systems, complex networks, Boolean networks, systems biology, control, canalization, redundancy