Acta Biotheoretica

, Volume 58, Issue 4, pp 341–353 | Cite as

Quantitative Modeling of Tumor Dynamics and Radiotherapy

  • Heiko EnderlingEmail author
  • Mark A. J. Chaplain
  • Philip Hahnfeldt
Regular Article


Cancer is a complex disease, necessitating research on many different levels; at the subcellular level to identify genes, proteins and signaling pathways associated with the disease; at the cellular level to identify, for example, cell-cell adhesion and communication mechanisms; at the tissue level to investigate disruption of homeostasis and interaction with the tissue of origin or settlement of metastasis; and finally at the systems level to explore its global impact, e.g. through the mechanism of cachexia. Mathematical models have been proposed to identify key mechanisms that underlie dynamics and events at every scale of interest, and increasing effort is now being paid to multi-scale models that bridge the different scales. With more biological data becoming available and with increased interdisciplinary efforts, theoretical models are rendering suitable tools to predict the origin and course of the disease. The ultimate aims of cancer models, however, are to enlighten our concept of the carcinogenesis process and to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often esoteric model renderings with inaccessible parameterization. Here we discuss some ideas for combining tractable dynamical tumor growth models with radiation response models using biologically accessible parameters to provide a more intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure.


Mathematical model Cellular automaton Radiotherapy Accelerated repopulation cancer stem cells 



The work was supported by the AACR Centennial Postdoctoral Fellowship in Cancer Research (08-40-02-ENDE, HE) and by a NASA/NSCOR grant (NNJ06HA28G, HE,PH). MAJC acknowledges the support of an ERC Advanced Investigator Grant No. 227619. The authors would also like to thank Afshin Beheshti for kindly providing the glioma radiation response images shown in Fig. 1d, and Russell Rockne and Benjamin Ribba for fruitful discussions.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Heiko Enderling
    • 1
    Email author
  • Mark A. J. Chaplain
    • 2
  • Philip Hahnfeldt
    • 1
  1. 1.Center of Cancer Systems Biology, Caritas St. Elizabeth’s Medical CenterTufts University School of MedicineBostonUSA
  2. 2.Division of MathematicsUniversity of DundeeNethergate, DundeeUK

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