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Mathematical oncology: Cancer summed up

Author

Listed:
  • Robert A. Gatenby

    (University of Arizona)

  • Philip K. Maini

    (the Centre for Mathematical Biology, Mathematical Institute)

Abstract

Clinical oncologists and tumour biologists possess virtually no comprehensive model to serve as a framework for understanding, organizing and applying their data.

Suggested Citation

  • Robert A. Gatenby & Philip K. Maini, 2003. "Mathematical oncology: Cancer summed up," Nature, Nature, vol. 421(6921), pages 321-321, January.
    • Handle: RePEc:nat:nature:v:421:y:2003:i:6921:d:10.1038_421321a
    DOI: 10.1038/421321a
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    Cited by:

    1. Horvath, Denis & Brutovsky, Branislav, 2016. "Etiology of phenotype switching strategy in time varying stochastic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 455-468.
    2. Benjamin Wölfl & Hedy te Rietmole & Monica Salvioli & Artem Kaznatcheev & Frank Thuijsman & Joel S. Brown & Boudewijn Burgering & Kateřina Staňková, 2022. "The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer," Dynamic Games and Applications, Springer, vol. 12(2), pages 313-342, June.
    3. Das, Parthasakha & Das, Samhita & Das, Pritha & Rihan, Fathalla A. & Uzuntarla, Muhammet & Ghosh, Dibakar, 2021. "Optimal control strategy for cancer remission using combinatorial therapy: A mathematical model-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    4. Abernethy, Sam & Gooding, Robert J., 2018. "The importance of chaotic attractors in modelling tumour growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 268-277.

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