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Semin Cancer Biol. 2015 Feb;30:70-8. doi: 10.1016/j.semcancer.2014.04.001. Epub 2014 May 2.

Simulating cancer growth with multiscale agent-based modeling.

Author information

1
Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: zwang@salud.unm.edu.
2
Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
3
Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
4
Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
5
ThinkMotu LLC, Wellesley, MA 02481, USA(2). Electronic address: ts.deisboeck@thinkmotu.com.

Abstract

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.

KEYWORDS:

Drug discovery; Mathematical modeling; Signaling pathway; Translational research; Tumor growth and invasion

PMID:
24793698
PMCID:
PMC4216775
DOI:
10.1016/j.semcancer.2014.04.001
[Indexed for MEDLINE]
Free PMC Article

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