ReviewNature reviews. Genetics2015
Quantitative and logic modelling of molecular and gene networks.
Review in Nature reviews. Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 180 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
180 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.bioRxiv : the preprint server for biology · 2026Article
- Refining treatment strategies for non-small cell lung cancer lacking actionable mutations: insights from multi-omics studies.British journal of cancer · 2025Review
- NeKo: A tool for automatic network construction from prior knowledge.PLoS computational biology · 2025Article
- Causality-aware graph neural networks for functional stratification and phenotype prediction at scale.NPJ systems biology and applications · 2025Article
- LM-Merger: a workflow for merging logical models with an application to gene regulatory network models.BMC bioinformatics · 2025Article
- Methods in quantitative biology-from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.Physical biology · 2025Review
- Sensitivity analysis on protein-protein interaction networks through deep graph networks.BMC bioinformatics · 2025Article
- FAIRification of computational models in biology.bioRxiv : the preprint server for biology · 2025Article
- Global Sensitivity Analysis of a Novel Signaling Network for Heart Growth With Local IGF1 Production.International journal for numerical methods in biomedical engineering · 2025Article
- Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD.Genes · 2024Article
- Aligning with the 3Rs: alternative models for research into muscle development and inherited myopathies.BMC veterinary research · 2024Review
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Molecular causality in the advent of foundation models.Molecular systems biology · 2024Review
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Correlations reveal the hierarchical organization of biological networks with latent variables.Communications biology · 2024Article
- Article
- Quantifying Landscape-Flux via Single-Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics.Journal of clinical medicine · 2024Review
- Phenotypic plasticity for improved light harvesting, in tandem with methylome repatterning in reef-building corals.Molecular ecology · 2024Article
120 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
1 author.
Funding
Abstract
Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.
Indexed as
Identifiers
What OpenQuestion holds
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.