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.
Highlights
Reviewed by: Jongrae Kim, University of Leeds, United Kingdom Anatoly Sorokin, Institute of Cell Biophysics (RAS), Russia
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)
We provide several predefined example Boolean Networks (BN) models that can be directly loaded: the Arabidopsis Thaliana gene regulatory network (GRN) of flowering patterns (Chaos et al, 2006), a simplified version of the segment polarity GRN of Drosophila melanogaster (Albert and Othmer, 2003), the Budding Yeast cell-cycle regulatory network (Li et al, 2004), and the BN motifs analyzed in Gates and Rocha (2016)
Summary
Mathematical and computational modeling of biological networks promises to uncover the fundamental principles of living systems in an integrative manner (Iyengar, 2009; Ideker and Nussinov, 2017). We present CANA1, a python package to study redundancy and control in BN models of biochemical dynamics (Correia et al, 2018). We provide an interface between CANA and the Cell Collective (Helikar et al, 2012), allowing for an extensive analysis of control and canalization in complex biological systems. The CANA package expands the set of available tools of the second category, by providing Python classes to calculate measures and visualizations of canalization (dynamical redundancy) and control of BN models, as detailed below. CANA is designed as a toolbox for both computational and experimental system biologists It enables the simplification of BN models and testing of network control algorithms, prioritizing biochemical variables more likely to be relevant for specific biological questions (e.g., genes controlling cell fate), and ideal candidates for knockout experiments
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