Monc: Data-driven medicine against cancer

Coupling models with data to address relevant challenges for biologists and clinicians

The Inria team MONC is devoted to applications of mathematical modeling, simulations and scientific calculus in oncology with the following aims:

  • Improve our understanding in cancer biology and pharmacology,
  • Assist the development of novel therapeutic approaches,
  • Develop personalized decision-helping tools for monitoring the disease and evaluating therapies.

More precisely, we aim at developing new mathematical models – involving partial differential equations (PDE) and built from a precise biological and medical knowledge – combined with novel data assimilation techniques, image processing, statistical methods and (artificial) intelligence – in order to build numerical tools based on available quantitative data about cancer. The goal is finally to be able to help clinicians and/or biologists to better understand, predict or control tumor growth and possibly evaluate the therapeutic response, in a clinical context or for pre-clinical studies. We plan to develop patient-specific approaches (mainly based on medical imaging) as well as population-type approaches in order to take advantage of clinical cohorts.

Each type of cancer is different and only a limited number of pathologies are targeted among which: lung metastases, meningioma, gliomas, soft-tissue sarcoma, kidney, lung and liver tumors.


Partners: CNRS, Institut Polytechnique de Bordeaux.

Collaborator: Institut de Mathématiques de Bordeaux (UMR 5251).

Comments are closed