Prize for transdisciplinary publication 2019

The prize for 2019 goes to a study where mathematical modelling and computer simulations are used to improve personalized treatment of breast cancer.

The award is given to research projects in the Centre that have published studies resulting from highly interdisciplinary research efforts. In addition, to stimulate visibility of the DLN research, there is a strict criterium for digital life funded projects, that the awarded publication(s) clearly states its association to the Centre for Digital Life Norway. We also encourage our partner projects to acknowledge their association with the Centre in their publications.

The 2019 prize for “Transdisciplinary publication of the year” was awarded to the partner project PerCaThe, for the paper “Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data”, published in Cancer Research. The publication is an excellent example of how computation and modelling can be applied to clinical data and move towards clinical applications and in silico trials.

The team behind the work consisted of researchers within clinical cancer medicine, imaging, animal models, molecular biology, mathematics and statistics as well as computational science. A nice display of their transdisciplinary efforts is actually depicted well in the picture above, where also the modellers are investigating histological sections from tumour biopsies.

A multi-scale modelling approach was chosen to simulate tumour responses to chemotherapeutic and antiangiogenic treatment of breast cancer patients. In the model, cancer cells, stroma cells and microvasculature were modelled as agents in a hybrid cellular automata, where the tumour tissue exchange nutrients and therapeutic agents according to estimated diffusive and convective properties. In addition to cancer cell division and death, some intracellular signalling responses was also included in the agents. The model was adapted to individual patients, using data from histology, magnetic resonance imaging and molecular markers to simulate the response to therapy. Read the full paper here: link.

The research team has also written in Aftenposten about the application of modelling and machine learning in cancer treatment:


Rune Kleppe



Tilknyttet prosjekt
By Rune Kleppe
Published Apr. 17, 2020 11:57 AM - Last modified Oct. 20, 2020 3:51 PM