ArticleEURASIP journal on bioinformatics & systems biology2015
Molecular network control through boolean canalization.
Article in EURASIP journal on bioinformatics & systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 34 citations in OpenAlex.
- A Dataset of Benchmark Boolean Models for Gene Regulatory Networks.Scientific data · 2026Article
- Phenotype Control techniques for Boolean gene regulatory networks.Bulletin of mathematical biology · 2023Review
- Phenotype control techniques for Boolean gene regulatory networks.bioRxiv : the preprint server for biology · 2023Article
- Boolean model of the gene regulatory network ofFrontiers in microbiology · 2023Article
- Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022Article
- Capturing dynamic relevance in Boolean networks using graph theoretical measures.Bioinformatics (Oxford, England) · 2021Article
- Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies.PLoS computational biology · 2021Article
- A Near-Optimal Control Method for Stochastic Boolean Networks.Letters in biomathematics · 2020Article
- Control of Intracellular Molecular Networks Using Algebraic Methods.Bulletin of mathematical biology · 2019Article
- Determining Relative Dynamic Stability of Cell States Using Boolean Network Model.Scientific reports · 2018Article
- The phenotype control kernel of a biomolecular regulatory network.BMC systems biology · 2018Article
- Target Control in Logical Models Using the Domain of Influence of Nodes.Frontiers in physiology · 2018Article
- Structure-based control of complex networks with nonlinear dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Identification of control targets in Boolean molecular network models via computational algebra.BMC systems biology · 2016Article
- Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms.Frontiers in neuroscience · 2016Article
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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
Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a
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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.