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Metastasis

Metastasis

Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...

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Related Experiment Video

Updated: Jun 4, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Predictive mathematical modeling in metastasis.

¹Department of Mathematics, Heriot Watt University, Edinburgh, UK.

Methods in Molecular Medicine

|February 23, 2011

Summary

This summary is machine-generated.

Mathematical modeling offers powerful predictive insights into cancer metastasis by creating accurate representations of cellular activities. This approach integrates molecular biology and nonlinear mathematics to forecast macroscopic outcomes from cell-level data.

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Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

Published on: May 30, 2011

Area of Science:

* Mathematical biology
* Computational oncology
* Systems medicine

Background:

* Mathematical modeling has evolved from early phenomenological approaches to mechanistic representations.
* Advances in molecular biology and nonlinear mathematics enable detailed, cell-level modeling.
* Historical successes, like Hodgkin-Huxley's work, highlight the predictive power of mathematical biology.

Purpose of the Study:

* To explore the growing role of mathematical modeling in predicting biological and medical phenomena, particularly cancer metastasis.
* To differentiate modern mechanistic modeling from traditional data-fitting techniques in cancer research.
* To underscore the impact of integrating molecular-level data with mathematical frameworks for macroscopic predictions.

Main Methods:

* Construction of mathematical models using experimental data at the cellular and molecular levels.
* Quantitative representation of specific cellular activities.
* Application of nonlinear mathematics to biological systems.

Main Results:

* Mathematical models provide effective and increasingly widespread applications in predicting cancer metastasis.
* This approach allows for the prediction of macroscopic implications from detailed molecular and cellular data.
* The methodology represents a shift towards understanding specific underlying biological mechanisms.

Conclusions:

* Mathematical modeling is a powerful predictive tool in biology and medicine, especially for cancer metastasis.
* Modern mathematical models, informed by molecular data, can predict macroscopic outcomes.
* This field integrates experimental biology with advanced mathematical techniques for mechanistic insights.