ArticlePLoS computational biology2018
Identifying (un)controllable dynamical behavior in complex networks.
Article in PLoS computational biology, 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.
- Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026Article
- Mapping the attractor landscape of Boolean networks with biobalm.Bioinformatics (Oxford, England) · 2025Article
- Macroscopic resting-state brain dynamics are best described by linear models.Nature biomedical engineering · 2024Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- Article
- Minimal frustration underlies the usefulness of incomplete regulatory network models in biology.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Reproducibility and FAIR principles: the case of a segment polarity network model.Frontiers in cell and developmental biology · 2023Article
- Leveraging network structure in nonlinear control.NPJ systems biology and applications · 2022Review
- Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks.Science advances · 2021Article
- Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy.PLoS computational biology · 2020Article
- Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction.Frontiers in physiology · 2020Article
- A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint.Scientific reports · 2019Article
- Brumadinho: between prudence and probability, tragedy.Revista brasileira de medicina do trabalho : publicacao oficial da Associacao Nacional de Medicina do Trabalho-ANAMT · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system "decision point", or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the system's repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response.
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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.