ArticleComputational and systems oncology2021
Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions.
Article in Computational and systems oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- Hallmarks of epithelial-mesenchymal plasticity in cancer.Molecular cancer · 2026Review
- Building dynamical models of multi-step state transitions from single cell gene expression trajectories.bioRxiv : the preprint server for biology · 2025Article
- A Multicellular Mechanochemical Model to Investigate Tumor Microenvironment Remodeling and Pre-Metastatic Niche Formation.Cellular and molecular bioengineering · 2024Article
- Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.NPJ systems biology and applications · 2024Article
- A quantitative evaluation of topological motifs and their coupling in gene circuit state distributions.iScience · 2023Article
- Article
- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
- Discrete modeling for integration and analysis of large-scale signaling networks.PLoS computational biology · 2022Article
- A multiscale computational model of YAP signaling in epithelial fingering behavior.Biophysical journal · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Epithelial-mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell-cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.
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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.