ArticlePLoS computational biology2022
Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Mathematical models of intercellular signaling in breast cancer.Seminars in cancer biology · 2025Review
- Oxygen, angiogenesis, cancer and immune interplay in breast tumour microenvironment: a computational investigation.Royal Society open science · 2024Article
- Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Article
- A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice.Journal of personalized medicine · 2022Article
- Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.Frontiers in digital health · 2022Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.
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Registered trials
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