Laurence Calzone – Cancer network modelling: towards personalised medicine

June 13th, 2019, 11:00 am

Personalised medicine is one of the challenges of systems biology. If some clinical trials have already explored the possibility to treat patients individually based on their genomic profiles, there are more and more attempts to use mathematical models as an aid to select therapies in the clinics. The purpose of these models is to describe, understand and predict cellular responses from a molecular perspective. They serve to explain complex biological phenomena, provide predictions that can be tested experimentally, and formulate plausible scenarios of a complex biological behaviour when intuition is not sufficient anymore.

The process of building these models ideally starts with the analyses of omics data and the gathering of prior information (from published experiments and databases) in the form of a network. The network is then translated into a mathematical model with the appropriate formalism that will depend on the initial biological question and the type of networks (chemical kinetics, logical approach, etc.). The model then aims at predicting and anticipating the effect of a perturbation, either from an intrinsic point of view (e.g., mutations) or from an extrinsic point of view (e.g. drug treatment). I will show some examples of such analyses with logical models of signalling pathways which are known to be altered in cancer.

Dr Laurence Calzone is a research scientist at Institut Curie. She has published mathematical models using several formalisms including nonlinear ordinary differential equations and, more recently, Boolean formalism to address specific biological questions such as: the timely organisation of the budding yeast cell cycle, the description of syncytial cycles in drosophila, cell fate decision processes in response to cell death signals, interplays between MAPK pathways, metastasis process, etc.

She has very good experience in constructing these models based on published articles and in analysing patient data using these models. She has also participated in developing methods to improve the simulations of the mathematical models she builds.

The presentation will be in English

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