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MONC Research team

Mathematical modeling for Oncology

Team presentation

The MONC project-team aims at developing new mathematical models involving partial differential equations and statistical methods based on a precise biological and medical knowledge 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 available large data bases. We claim that our work may have a clinical impact that can change the way of handling certain pathologies. 

Research themes

The team is working around 3 axes of research

  • Axis 1: Tumor modeling for patient-specific simulations.

  • Axis 2: Bio-physical modeling for personalized therapies.

  • Axis 3: Quantitative cancer modeling for biological and preclinical studies.

In the first axis, we aim at producing patient-specific simulations of the growth of a tumor or its response to treatment starting from a series of images. We hope to be able to give information to the clinicians in order to improve the decision process. It will be mainly useful in the case of a relapse or for metastatic diseases.

The second axis aims at modeling the biophysical therapies like radiotherapies, but also thermo-ablations, radio-frequency ablations or electroporation that play a crucial role in the case of a relapse or for a metastatic disease, which is precisely the clinical context where the techniques of axis 1 will be applied.

The third axis, even if not directly linked to clinical perspectives, is essen- tial since it is a way to better understand and model the biological reality of cancer growth and the (possibly complex) effects of therapeutic intervention. Modeling in this case also helps to interpret the experimental results and im- prove the accuracy of the models used in Axis 1. Technically speaking, some of the computing tools are similar to those of axis 1.

International and industrial relations

Local collaborators

  • Institut Bergonié, Bordeaux, France.
  • CHU Bordeaux, France
  • Angiogenesis and cancer microenvironment laboratory, Inserm U1029, Bordeaux, France.

National and international collaborators

  • Neuro-oncology department, University of Alabama at Birmingham, USA.
  • ITAV, Toulouse, France.
  • Vectorology and Anticancerous therapies lab at Institut Gustave Roussy, France.
  • CHU J. Verdier, Bondy, France.
  • Roswell Park Cancer Institute, Buffalo, NY, USA.
  • SMARTc unit of the Center for Research in Oncobiology and Oncopharmacology (CRO2) in Marseille (Inserm UMR S 911), France.
  • Humanitas Research Hospital, Milan, Italy.

Keywords: Modeling (PDE ODE) ; Multiscale modeling ; Statistical methods ; Optimization ; Data assimilation and processing ; Reduced order models ; Computational Biology ; System Biology ; Cancer ; Resistance ; Medical and biological Imaging