Mathematics of cancer

The development of cancer treatments and therapeutic interventions is subject to a great deal of uncertainty and involves a fair amount of trial and error. This reduces the potential clinical benefits and also causes costs to shoot up. The outcome from currently available treatments can be improved, for instance by optimizing the schedules, dosage, and combinations with other therapies. Clinical trials on their different phases can also be predesigned virtually to produce better results with lower investment. We describe treatment effects with mathematical models that we then use to conduct in silico clinical trials and explore modifications. This allows us to identify the best results in terms of resistance, survival, and toxicity.


Phenotypically structured PDE models for the scaling of the metabolic hotspot of activity in cancer.

Optimal control of chemotherapy for low-grade gliomas.

Dynamical system analysis of the feedback signaling in B lymphopoiesis.

Role of stochastic fluctuations in the non-genetic evolution of clonal cell populations.

Nonlinear wave FKPP-like model for the analysis of the proliferative rim width in glioblastoma.


There are no publications on this topic yet, but they are coming