ArticleFrontiers in molecular biosciences2020
Toward Modeling Context-Specific EMT Regulatory Networks Using Temporal Single Cell RNA-Seq Data.
Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 40 citations in OpenAlex.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression.NPJ systems biology and applications · 2026Article
- sRACIPE 2.0: a systems biology circuit modeling toolkit for random circuit perturbation.Bioinformatics (Oxford, England) · 2026Article
- Building dynamical models of multi-step state transitions from single cell gene expression trajectories.bioRxiv : the preprint server for biology · 2025Article
- Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.Biophysical journal · 2024Article
- Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Interaction of NF-κB and FOSL1 drives glioma stemness.Cellular and molecular life sciences : CMLS · 2024Article
- Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML.NPJ systems biology and applications · 2024Article
- Theoretical and computational tools to model multistable gene regulatory networks.Reports on progress in physics. Physical Society (Great Britain) · 2023Review
- A quantitative evaluation of topological motifs and their coupling in gene circuit state distributions.iScience · 2023Article
- Article
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
- What Makes a Functional Gene Regulatory Network? A Circuit Motif Analysis.The journal of physical chemistry. B · 2022Article
- Comparative single-cell transcriptomes of dose and time dependent epithelial-mesenchymal spectrums.NAR genomics and bioinformatics · 2022Article
- SMAD4 mutations do not preclude epithelial-mesenchymal transition in colorectal cancer.Oncogene · 2022Article
- Review
- Editorial: Epithelial to Mesenchymal Plasticity in Colorectal Cancer.Frontiers in cell and developmental biology · 2022Article
- Gene Circuit Explorer (GeneEx): an interactive web-app for visualizing, simulating and analyzing gene regulatory circuits.Bioinformatics (Oxford, England) · 2021Article
- Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions.Computational and systems oncology · 2021Article
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 well established as playing a crucial role in cancer progression and being a potential therapeutic target. To elucidate the gene regulation that drives the decision making of EMT, many previous studies have been conducted to model EMT gene regulatory circuits (GRCs) using interactions from the literature. While this approach can depict the generic regulatory interactions, it falls short of capturing context-specific features. Here, we explore the effectiveness of a combined bioinformatics and mathematical modeling approach to construct context-specific EMT GRCs directly from transcriptomics data. Using time-series single cell RNA-sequencing data from four different cancer cell lines treated with three EMT-inducing signals, we identify context-specific activity dynamics of common EMT transcription factors. In particular, we observe distinct paths during the forward and backward transitions, as is evident from the dynamics of major regulators such as NF-KB (e.g., NFKB2 and RELB) and AP-1 (e.g., FOSL1 and JUNB). For each experimental condition, we systematically sample a large set of network models and identify the optimal GRC capturing context-specific EMT states using a mathematical modeling method named
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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.