ReviewFrontiers in genetics2016
Logical Modeling and Dynamical Analysis of Cellular Networks.
Review in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
What it found
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Who cites it
114 citing papers in PubMed, 244 citations in OpenAlex.
- Design of a Novel Diamine Biosensor to Guide the Engineering of Escherichia coli for Cadaverine Overproduction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Applying prior knowledge of regulatory signaling to investigate macrophage cAMP dynamics during Mycobacterium tuberculosis infection.Scientific reports · 2026Article
- Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026Article
- GPU-accelerated modeling of biological regulatory networks.Scientific reports · 2026Article
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.bioRxiv : the preprint server for biology · 2026Article
- The Emergent Architecture of Autonomous Clocks Described by Computational Algorithms of Regulatory Networks.Advances in experimental medicine and biology · 2026Article
- A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases.Frontiers in immunology · 2026Article
- p53 Orchestrates the Immunogenic-Tolerogenic Pyroptosis Switch in Non-Small Cell Lung Cancer: A Systems Biology Approach.Computational and structural biotechnology journal · 2026Article
- Petri net on expanded networks computes the dynamics of finite multilevel models.Interface focus · 2025Article
- Reproducible Boolean model analyses and simulations with the CoLoMoTo software suite: a tutorial.Interface focus · 2025Article
- LM-Merger: a workflow for merging logical models with an application to gene regulatory network models.BMC bioinformatics · 2025Article
- Automated model refinement using perturbation-observation pairs.NPJ systems biology and applications · 2025Article
- An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks?Journal of mathematical biology · 2025Article
- Targeting NSCLC drug resistance: Systems biology insights into the MALAT1/miR-145-5p axis and Wip1 in regulating ferroptosis and apoptosis.Journal of the Royal Society, Interface · 2025Article
- PseudoCell: A Multivalued Logical Regulatory Network to Investigate Premature Senescence Dynamics and Heterogeneity.Aging medicine (Milton (N.S.W)) · 2025Article
- DNA Damage-Induced Ferroptosis: A Boolean Model Regulating p53 and Non-Coding RNAs in Drug Resistance.Proteomes · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Mathematical basis and toolchain for hierarchical optimization of biochemical networks.PLoS computational biology · 2024Article
54 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.
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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.