ArticleFrontiers in physiology2018
Target Control in Logical Models Using the Domain of Influence of Nodes.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Node and edge control strategy identification via trap spaces in Boolean networks.BMC bioinformatics · 2025Article
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors.Alzheimer's research & therapy · 2024Article
- Canalizing kernel for cell fate determination.Briefings in bioinformatics · 2024Article
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
- A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.PLoS biology · 2024Article
- Dynamical methods for target control of biological networks.Royal Society open science · 2023Article
- Phenotype Control techniques for Boolean gene regulatory networks.Bulletin of mathematical biology · 2023Review
- Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Phenotype control techniques for Boolean gene regulatory networks.bioRxiv : the preprint server for biology · 2023Article
- Global stabilizing control of large-scale biomolecular regulatory networks.Bioinformatics (Oxford, England) · 2023Article
- NETISCE: a network-based tool for cell fate reprogramming.NPJ systems biology and applications · 2022Article
- A Family of Fitness Landscapes Modeled through Gene Regulatory Networks.Entropy (Basel, Switzerland) · 2022Article
- Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022Article
- Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks.Science advances · 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
- A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.Frontiers in molecular biosciences · 2020Article
- Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction.Frontiers in physiology · 2020Article
- Control of Intracellular Molecular Networks Using Algebraic Methods.Bulletin of mathematical biology · 2019Article
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3 authors.
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Abstract
Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacological inhibition of proteins, and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the domain of influence (DOI) of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the logical domain of influence (LDOI) of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose DOI can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to
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