ArticleFrontiers in physiology2018
CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 30 citations in OpenAlex.
- CANA v1.0.0: efficient quantification of canalization in automata networks.Bioinformatics (Oxford, England) · 2025Article
- An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?Journal of mathematical biology · 2025Article
- Mapping the attractor landscape of Boolean networks with biobalm.Bioinformatics (Oxford, England) · 2025Article
- Models of Cell Processes are Far from the Edge of Chaos.PRX life · 2023Article
- Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks.Entropy (Basel, Switzerland) · 2023Article
- Global stabilizing control of large-scale biomolecular regulatory networks.Bioinformatics (Oxford, England) · 2023Article
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- On the feasibility of dynamical analysis of network models of biochemical regulation.Bioinformatics (Oxford, England) · 2022Article
- Boolean modelling as a logic-based dynamic approach in systems medicine.Computational and structural biotechnology journal · 2022Review
- Effective connectivity determines the critical dynamics of biochemical networks.Journal of the Royal Society, Interface · 2022Article
- Identifying "more equal than others" edges in diverse biochemical networks.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Control of Intracellular Molecular Networks Using Algebraic Methods.Bulletin of mathematical biology · 2019Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
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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
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