Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (613 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Interacting DNA-encoded regulatory subsystems in the genome that coordinate input from activator and repressor transcription factors during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of genes for specific conditions, times, or locations.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Gene Regulatory Network; Network, Gene Regulatory; Networks, Gene Regulatory; Regulatory Network, Gene; Regulatory Networks, Gene; Gene Networks; Gene Network; Network, Gene.
Where it fits in the MeSH classification
- Broader topics
- Regulatory Sequences, Nucleic Acid
- See also
- Gene Expression Regulation; Protein Interaction Maps
Related concepts
Research landscape
PubMed indexes 40,116 records tagged with the MeSH heading “Gene Regulatory Networks” (counts retrieved 2026-10-29). 17,942 of them (44.7%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 20,471 records.
Reviews account for 4,151 records (10.3%) and meta-analyses for 331. Randomized controlled trials number 54 (0.1%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 2,157 |
| 2010-2019 | 20,471 |
| 2020 onward | 17,942 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 4,151 | 10.3% |
| Meta-analyses | 331 | 0.8% |
| Randomized controlled trials | 54 | 0.1% |
Method: PubMed query "Gene Regulatory Networks"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in genetics and genomics
Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| An encyclopedia of human enhancer-gene regulatory interactions | 2026 | Research article Design not recorded in structured metadata | 32 | cc by PMID 42457959 · DOI · PMC13471189 |
| Gene regulatory networks: from correlative models to causal explanations | 2026 | Review article Design not recorded in structured metadata | 9 | cc by PMID 41803457 · DOI · PMC7618986 |
| CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data | 2026 | Research article Design not recorded in structured metadata | 8 | cc by PMID 42225598 · DOI · PMC13242928 |
An encyclopedia of human enhancer-gene regulatory interactions [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease1-6.
- Closing excerpt
- These genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (239 words)
Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the effect of human genetic variation on disease1-6. Here we create and evaluate a resource of more than 92 million enhancer-gene regulatory interactions across 1,458 biosamples covering 369 cell types and tissues, by integrating predictive models, chromatin states, three-dimensional contacts and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,356 element-gene pairs measured in CRISPR perturbation experiments, more than 30,000 fine-mapped expression quantitative trait loci and 569 fine-mapped genome-wide association study (GWAS) variants linked to a probable causal gene. Using this framework, we develop ENCODE-rE2G, a predictive model achieving state-of-the-art performance across several prediction tasks, demonstrating that iterative perturbations and supervised machine learning can build increasingly accurate predictive models of enhancer regulation. Using ENCODE-rE2G, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, revealing global properties of enhancer networks, identifying differences in regulatory complexity across genes and improving analyses linking noncoding variants to target genes and cell types for common complex diseases. By interpreting the model, we find that beyond enhancer activity and three-dimensional enhancer-promoter contacts, additional features that guide enhancer-promoter communication include promoter class and enhancer-enhancer synergy. These genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42457959 · DOI · PMC13471189
Gene regulatory networks: from correlative models to causal explanations [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Gene regulatory networks (GRNs) explain how the genome controls cellular behaviour and tissue morphogenesis, serving to connect molecular mechanism to functional output.
- Closing excerpt
- By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (177 words)
Gene regulatory networks (GRNs) explain how the genome controls cellular behaviour and tissue morphogenesis, serving to connect molecular mechanism to functional output. Single-cell technologies now provide descriptions of these networks with unprecedented detail, but this advance has also revealed gene regulatory systems that are too complex for our existing conceptual frameworks. GRNs, which should provide mechanistic explanations, are increasingly reduced to statistical correlations - 'hairballs' that fail to capture molecular causation. Here, we explore why this dilemma exists and propose a path forward. We argue that methods in 'representation learning' can be used to model GRNs, without needing to capture every molecular detail. For this framework, we advocate three linked principles: models must be inherently mechanistic, with structures grounded in cellular and evolutionary biology; molecular principles and constraints must be used to reduce the solution space for learning GRN models; and more sophisticated forms of experimental perturbation and synthetic biological engineering are needed to train models and test predictions. By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41803457 · DOI · PMC7618986
CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data [3]
Abstract reading summary — source excerpts
- Opening excerpt
- MotivationSingle-cell sequencing technologies allow researchers to study cell-cell variation within a cell population.
- Closing excerpt
- com/PeterZZQ/CeSpGRN.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (197 words)
MotivationSingle-cell sequencing technologies allow researchers to study cell-cell variation within a cell population. Variations between cells are driven by the underlying biological network, particularly gene regulatory networks (GRNs). GRNs rewire as cells evolve, and different cells can have different GRNs. However, while single-cell RNA-sequencing (scRNA-seq) and single-cell multi-omics data have been used to reconstruct GRNs, the output GRNs are rarely cell-specific, but rather, most existing methods infer population-level or cell-type-level GRNs.ResultsWe propose CeSpGRN (Cell-Specific Gene Regulatory Network inference), a method that infers cell-specific GRNs from scRNA-seq, paired scRNA-seq and scATAC-seq, or spatial transcriptomic data. In particular, existing methods that use matching scRNA-seq and scATAC-seq data incorporate population-level region information in GRN inference, whereas CeSpGRN utilizes single-cell resolution region information. CeSpGRN infers cell-specific GRNs using a kernel-weighted Gaussian Copula Graphical Model, and incorporates multi-omic or spatial location information when constructing the objective function. We tested CeSpGRN on both simulated and real datasets, and the results show that CeSpGRN has a superior performance compared to baseline methods in reconstructing GRNs and detecting regulatory interactions that differ between cells. CeSpGRN uncovered regulatory interactions that rewire during biological processes on real datasets.Availability and implementationCeSpGRN is a Python package available at https://github.com/PeterZZQ/CeSpGRN.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42225598 · DOI · PMC13242928
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article The Flavonoid Biosynthesis Network in Plants [4]
- Research article Gene regulatory network inference in the era of single-cell multi-omics [5]
- Research article Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression [6]
- Research article Atrial Fibrillation: Pathogenesis, Predisposing Factors, and Genetics [7]
- Research article Regulatory networks in plant responses to drought and cold stress [8]
- Research article The sound of silence: Transgene silencing in mammalian cell engineering [9]
References
- Gschwind AR, Mualim KS, Karbalayghareh A, Sheth MU, Dey KK, Jagoda E, et al.. An encyclopedia of human enhancer-gene regulatory interactions. . 2026. PMID 42457959 · DOI · PMC13471189
- Maizels RJ, Briscoe J. Gene regulatory networks: from correlative models to causal explanations. . 2026. PMID 41803457 · DOI · PMC7618986
- Zhang Z, Han J, Song L, Zhang X. CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data. . 2026. PMID 42225598 · DOI · PMC13242928
- Liu W, Feng Y, Yu S, Fan Z, Li X, Li J, et al.. The Flavonoid Biosynthesis Network in Plants. Int J Mol Sci. 2021. PMID 34884627 · DOI · PMC8657439
- Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, et al.. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet. 2023. PMID 37365273 · DOI
- Lüönd F, Tiede S, Christofori G. Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression. Br J Cancer. 2021. PMID 33824479 · DOI · PMC8292450
- Sagris M, Vardas EP, Theofilis P, Antonopoulos AS, Oikonomou E, Tousoulis D. Atrial Fibrillation: Pathogenesis, Predisposing Factors, and Genetics. Int J Mol Sci. 2021. PMID 35008432 · DOI · PMC8744894
- Kim JS, Kidokoro S, Yamaguchi-Shinozaki K, Shinozaki K. Regulatory networks in plant responses to drought and cold stress. Plant Physiol. 2024. PMID 38514098 · DOI · PMC11060690
- Cabrera A, Edelstein HI, Glykofrydis F, Love KS, Palacios S, Tycko J, et al.. The sound of silence: Transgene silencing in mammalian cell engineering. Cell Syst. 2022. PMID 36549273 · DOI · PMC9880859
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.