ReviewCell communication and signaling : CCS2013
Recent development and biomedical applications of probabilistic Boolean networks.
Review in Cell communication and signaling : CCS, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
25 citing papers in PubMed.
- KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.bioRxiv : the preprint server for biology · 2026Article
- LM-Merger: a workflow for merging logical models with an application to gene regulatory network models.BMC bioinformatics · 2025Article
- pastboon: an R package to simulate parameterized stochastic Boolean networks.Bioinformatics advances · 2025Article
- Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.Genes · 2024Article
- Verifiable biology.Journal of the Royal Society, Interface · 2023Review
- Optimal Recursive Expert-Enabled Inference in Regulatory Networks.IEEE control systems letters · 2023Article
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- Modelling brain dynamics by Boolean networks.Scientific reports · 2022Article
- Probabilistic edge weights fine-tune Boolean network dynamics.PLoS computational biology · 2022Article
- Degeneracy measures in biologically plausible random Boolean networks.BMC bioinformatics · 2022Article
- Boolean modelling in plant biology.Quantitative plant biology · 2022Review
- WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically.Frontiers in molecular biosciences · 2021Article
- Exact solving and sensitivity analysis of stochastic continuous time Boolean models.BMC bioinformatics · 2020Article
- Simulating heterogeneous populations using Boolean models.BMC systems biology · 2018Article
- CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer.PLoS computational biology · 2018Article
- Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018Review
- Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.Frontiers in physiology · 2018Article
- Model checking optimal finite-horizon control for probabilistic gene regulatory networks.BMC systems biology · 2017Article
- FALCON: a toolbox for the fast contextualization of logical networks.Bioinformatics (Oxford, England) · 2017Article
- Inference of Biochemical S-Systems via Mixed-Variable Multiobjective Evolutionary Optimization.Computational and mathematical methods in medicine · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered.A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed.A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.
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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.