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Related Experiment Video

Updated: Jun 26, 2026

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Mathematical modeling and quantitative analysis of phenotypic plasticity during tumor evolution based on single-cell

Yuyang Xiao1, Xiufen Zou2

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.

Journal of Mathematical Biology
|August 20, 2024
PubMed
Summary

This study introduces a mathematical model to understand how cell plasticity and heterogeneity drive tumor growth. Faster cell changes correlate with increased malignancy, offering new targets for cancer therapies.

Keywords:
Mathematical modelPhenotypic heterogeneity and plasticityQuantitative analysisTumor malignancyWave speed

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Area of Science:

  • Mathematical Biology
  • Cancer Research
  • Computational Biology

Background:

  • Tumor progression is driven by complex cellular mechanisms, including plasticity and heterogeneity.
  • Understanding these dynamics is key to developing effective cancer treatments.

Purpose of the Study:

  • To develop a novel mathematical framework to investigate the role of cellular plasticity and heterogeneity in tumor progression.
  • To quantify cell phenotype transitions and their impact on tumor malignancy.

Main Methods:

  • Developed a reaction-convection-diffusion model using temporal single-cell data.
  • Applied theoretical analysis, including bifurcation analysis and AddModuleScore, to study tumor cell and macrophage dynamics.
  • Introduced pulse wave speed and high-plasticity/low-plasticity cell ratio as quantitative measures.

Main Results:

  • Established pulse wave speed as a measure of cell phenotype transition rate.
  • Identified a high-plasticity/low-plasticity cell ratio as an indicator of tumor malignancy.
  • Demonstrated that increased phenotype transition rates correlate with heightened malignancy and tumor progression to adenocarcinoma.

Conclusions:

  • Cellular plasticity significantly influences tumor progression and malignancy.
  • The developed mathematical framework and quantitative measures provide insights into tumor dynamics.
  • Findings can guide the development of targeted therapies to control tumor progression by regulating cellular plasticity.