ArticleCancers2020
A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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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.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling.Bioinformatics (Oxford, England) · 2026Article
- Breast cancer pathogenesis, diagnosis and treatment: a comprehensive review.Frontiers in oncology · 2026Review
- Emerging hallmarks and the rise of complexities and heterogeneity of tumor.Biochemistry and biophysics reports · 2025Review
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Positive and negative feedback regulation of the TGF-β1 explains two equilibrium states in skin aging.iScience · 2024Article
- Article
- Htr2b Promotes M1 Microglia Polarization and Neuroinflammation after Spinal Cord Injury via Inhibition of Neuregulin-1/ErbB Signaling.Molecular neurobiology · 2024Article
- Extending BioMASS to construct mathematical models from external knowledge.Bioinformatics advances · 2024Article
- Protocol for stratification of triple-negative breast cancer patients usingSTAR protocols · 2022Article
- Article
- A text-based computational framework for patient -specific modeling for classification of cancers.iScience · 2022Article
- Systems approaches to investigate the role of NF-κB signaling in aging.The Biochemical journal · 2022Review
- A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.
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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.