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BCM – Computational Biology and Modelisation

Disciplines Biology, Computer ScienceMathematics, Physics
Research fields

Biology : Cellular biology, Molecular biology, Cancerology, Epidemiology, Evolution, Metabolism, Cellular and tissular microscopy, Morphogenesis, Regulation network

Computer science: Scientific computationArtificial evolution,Individual-based models,  Simulation

Mathematics: Partial differential equations, Ordinary differential equations, Hybrid multi-scale models, Statistical models, Dynamical systems, topology

Physics: BiophysicsBiomechanics, active matter, multi-physics modelling

Supporting organisms Université Grenoble-Alpes CNRS, VetAgro Sup, Grenoble-INP
Geographical location Domaine de la Merci (La Tronche)

Laboratoire TIMC – Translational Innovation in Medicine and Complexity

Team leader

Angélique Stéphanou



The BCM team is a multidisciplinary team that is interested in the issue of emergence in biology including active matter, morphogenesis, evolution, differentiation, by implementing theoretical and experimental models via microscopy from cell to tissue. 

We are particularly interested in carcinogenesis, from the causes of the emergence of the tumor cell – by testing the hypotheses of the atavistic theory of cancer via artificial systems of evolutionary Boolean networks – to the mechanisms responsible for tumor development and invasion in the context of changing physico-chemical (metabolism) and mechanical properties of the microenvironment. The modeling of certain epigenetic mechanisms (marking of histones) is a subject developed in the group, in particular the epigenetic-metabolism coupling. 

Our study subjects also include cardiac morphogenesis, mammary gland morphogenesis, plant morphogenesis, through the interpretation of experimental data with topological or multiphysics models. 

The team also analyzes epidemiological and demographic data from surveys, cohorts, the cancer registry and other public databases. We develop statistical modeling methods in clinical or population epidemiology and in public health, for the assessment of risks for populations or the estimation of over-diagnosis.