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Encyclopedia of Complexity and Systems Science pp 1–13Cite as

Cellular Automaton Modeling of Tumor Invasion

Definition of the Subject

Cancer cells acquire characteristic traits in a stepwise manner during carcinogenesis. Some of these traits are autonomous growth, induction of angiogenesis, invasion, and metastasis. In this chapter, the focus is on one of the late stages of tumor progression: tumor invasion. Tumor invasion has been recognized as a complex system, since its behavior emerges from the combined effect of tumor cell-cell and cell-microenvironment interactions. Cellular automata (CA) provide simple models of self-organizing complex systems in which collective behavior can emerge out of an ensemble of many interacting “simple” components. Recently, cellular automata have been used to gain a deeper insight in tumor invasion dynamics. In this entry, we briefly introduce cellular automata as models of tumor invasion and we critically review the most prominent CA models of tumor invasion.

Introduction

Cancer describes a group of genetic and epigenetic diseases characterized by...

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Abbreviations

Cadherins:

Important class of transmembrane proteins. They play a significant role in cell-cell adhesion, ensuring that cells within tissues are bound together.

Chemotaxis:

Motion response to chemical concentration gradients of a diffusive chemical substance.

Extracellular matrix (ECM):

Components that are extracellular and composed of secreted fibrous proteins (e.g., collagen) and gel-like polysaccharides (e.g., glycosaminoglycans) binding cells and tissues together.

Fiber tracts:

Bundle of nerve fibers having a common origin, termination, and function and especially one within the spinal cord or brain.

Haptotaxis:

Directed motion of cells along adhesion gradients of fixed substrates in the ECM, such as integrins.

Slime trail motion:

Cells secrete a non-diffusive substance; concentration gradients of the substance allow the cells to migrate toward already explored paths.

Somatic evolution:

Darwinian-type evolution that occurs on soma (as opposed to germ) cells and characterizes cancer progression (Bodmer 1997).

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Acknowledgments

We are grateful to D. Basanta, L. Brusch, A. Chauviere, E. Flach, and F. Peruani for the comments and the fruitful discussions. We acknowledge support from the systems biology network HepatoSys of the German Ministry of Education and Research through grant 0313082 J. Andreas Deutsch is a member of the DFG Research Center for Regenerative Therapies Dresden – Cluster of Excellence – and gratefully acknowledges support from the center. The research was supported in part by funds from the EU Marie Curie Network “Modeling, Mathematical Methods and Computer Simulation of Tumor Growth and Therapy” (EU-RTD IST-2001-38923). Finally, the authors would like to thank for the financial support of the Gottfried Daimler and Karl Benz Foundation through the project “Biologistics: From bio-inspired engineering of complex logistical systems until nanologistics” (25-02/07).

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Authors and Affiliations

  1. Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany

    Haralambos Hatzikirou & Andreas Deutsch

  2. Institute of Pathology, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Georg Breier

Authors
  1. Haralambos Hatzikirou
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  2. Georg Breier
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  3. Andreas Deutsch
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Corresponding author

Correspondence to Haralambos Hatzikirou .

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Editors and Affiliations

  1. Ramtech Ltd., Larkspur, California, USA

    Robert A. Meyers

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Hatzikirou, H., Breier, G., Deutsch, A. (2014). Cellular Automaton Modeling of Tumor Invasion. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_60-5

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  • DOI: https://doi.org/10.1007/978-3-642-27737-5_60-5

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  1. Latest

    Cellular Automaton Modeling of Tumor Invasion
    Published:
    20 March 2020

    DOI: https://doi.org/10.1007/978-3-642-27737-5_60-6

  2. Original

    Cellular Automaton Modeling of Tumor Invasion
    Published:
    07 October 2014

    DOI: https://doi.org/10.1007/978-3-642-27737-5_60-5

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