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Cancer is a complex disease in which a variety of factors interact over a wide range of spatial and temporal scales with huge datasets relating to the different scales available. However, these data do not always reveal the mechanisms underpinning the observed phenomena. In this paper, we explain why mathematics is a powerful tool for interpreting such data by presenting case studies that illustrate the types of insight that realistic theoretical models of solid tumour growth may yield. These range from discriminating between competing hypotheses for the formation of collagenous capsules associated with benign tumours to predicting the most likely stimulus for protease production in early breast cancer. We will also illustrate the benefits that may result when experimentalists and theoreticians collaborate by considering a novel anti-cancer therapy.

Original publication

DOI

10.1098/rsta.2006.1786

Type

Journal article

Journal

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Publisher

The Royal Society

Publication Date

15/06/2006

Volume

364

Pages

1563 - 1578