ArticleFrontiers in physiology2018
Automatic Screening for Perturbations 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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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- CLL to Richter syndrome: Integrating network strategies with experiments elucidating disease drivers and personalized therapies.Science advances · 2025Article
- Boolean network modeling and its integration with experimental read-outs : An interdisciplinary presentation using a leukemia model.Pathologie (Heidelberg, Germany) · 2024Review
- GatekeepR: an R Shiny application for the identification of nodes with high dynamic impact in Boolean networks.Bioinformatics (Oxford, England) · 2024Article
- Exploring attractor bifurcations in Boolean networks.BMC bioinformatics · 2022Article
- Boolean modelling in plant biology.Quantitative plant biology · 2022Review
- Discrete Logic Modeling of Cell Signaling Pathways.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment.Frontiers in immunology · 2022Article
- Review
- Unraveling the Molecular Tumor-Promoting Regulation of Cofilin-1 in Pancreatic Cancer.Cancers · 2021Article
- Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysis.Frontiers in immunology · 2021Article
- Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells.Computational and structural biotechnology journal · 2021Article
- Concepts in Boolean network modeling: What do they all mean?Computational and structural biotechnology journal · 2020Review
- Representing dynamic biological networks with multi-scale probabilistic models.Communications biology · 2019Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.
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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.