ArticleNature communications2020
Reconciling qualitative, abstract, and scalable modeling of biological networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026Article
- Unveiling toxicological adverse outcomes: toward construction and simulation of large-scale networks.Toxicological sciences : an official journal of the Society of Toxicology · 2026Review
- Data driven network inference and longitudinal transcriptomics unveil dynamic regulation in Chronic Lymphocytic Leukaemia models.NPJ systems biology and applications · 2026Article
- Sketchbook: logical model inference from Boolean network sketches.Bioinformatics advances · 2026Article
- Path-based quantification of activation and repression in Boolean models using BooLEVARD.NPJ systems biology and applications · 2025Article
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- CLL to Richter syndrome: Integrating network strategies with experiments elucidating disease drivers and personalized therapies.Science advances · 2025Article
- Petri net on expanded networks computes the dynamics of finite multilevel models.Interface focus · 2025Article
- Learning interpretable network dynamics via universal neural symbolic regression.Nature communications · 2025Article
- Automated model refinement using perturbation-observation pairs.NPJ systems biology and applications · 2025Article
- An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?Journal of mathematical biology · 2025Article
- Refining Boolean models with the partial most permissive scheme.Bioinformatics (Oxford, England) · 2025Article
- Boolean network modeling and its integration with experimental read-outs : An interdisciplinary presentation using a leukemia model.Pathologie (Heidelberg, Germany) · 2024Review
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- Models of Cell Processes are Far from the Edge of Chaos.PRX life · 2023Article
- On the History of Ecosystem Dynamical Modeling: The Rise and Promises of Qualitative Models.Entropy (Basel, Switzerland) · 2023Article
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Trap spaces of multi-valued networks: definition, computation, and applications.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting biological systems' behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.