Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Mathematical oncology aims to contribute to the better understanding of cancer initiation, progression, and treatment, through the integration and application of mathematical models and simulations. Modelling cancer has become more feasible in recent decades, due to a combination of theoretical and technical advances, ever-expanding computational power, and a growing wealth of both experimental and clinical data. Mathematical models can be used to simulate tumour growth both forwards and backwards in time, and in response to treatments, improve our knowledge of individual cell behaviour, identify key features from complex data sets and clinical imaging, and ultimately develop patient-specific therapies and personalized medicine.
This Collection offers a platform for original contributions on mathematical oncology, from methodological advancements to applied clinical research, including integrative and multidisciplinary studies.