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Modeling Growth of Tumors and Their Spreading Behavior Using Mathematical Functions.

Bertin Hoffmann1, Thorsten Frenzel2, Rüdiger Schmitz2

  • 1Competence Center Bioinformatics, Institute for Applied Computer Science, University of Applied Sciences Stralsund, Stralsund, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|November 1, 2018
PubMed
Summary

This study focuses on computer simulations for modeling cancer metastasis. It details selecting mathematical models and parameters for accurate tumor growth simulation in xenograft mouse models, addressing experimental errors.

Keywords:
Mathematical modelMetastatic progressionParametrizationSpreading behaviorTumor growth

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Area of Science:

  • Computational biology
  • Cancer research
  • Mathematical modeling

Background:

  • Computer simulations are crucial for understanding cancer metastasis.
  • Accurate modeling requires appropriate mathematical functions and parameterization.
  • Experimental errors complicate the assessment of metastatic progression.

Purpose of the Study:

  • To guide the selection of mathematical models for tumor growth.
  • To provide methods for parameterizing these models in xenograft mouse models.
  • To address challenges in modeling metastatic progression.

Main Methods:

  • Selecting appropriate mathematical functions for tumor growth.
  • Parametrizing models using experimental data from xenograft mouse models.
  • Addressing fractal dimension of tumor vasculature and cell survival in circulation.

Main Results:

  • Demonstrates a systematic approach to selecting and parametrizing mathematical models for tumor growth.
  • Identifies common pitfalls in modeling metastatic progression.
  • Offers methods to mitigate experimental errors in model development.

Conclusions:

  • The chapter provides a framework for robust computational modeling of cancer metastasis.
  • Accurate parameterization and model selection are key to reliable simulation results.
  • This work aids researchers in developing more precise predictive models of tumor spread.