Cancer Network Modelling: towards precision medicine

Informations générales
Nom
Calzone
Prénom
Laurence
Diplôme
HDR
Année
2019
Détails de la thèse/HDR
Jury
Reka ALBERT
Franck DELAPLACE
Ioannis XENARIOS
Emmanuel BARILLOT
Anne SIEGEL
Véronique STOVEN
Denis THIEFFRY
Résumé en anglais
Mathematical models are becoming unavoidable in precision medicine. Their purpose is to describe, understand and predict cellular responses from a molecular perspective. Even when the mathematical models based on gene or protein networks are simple, they allow to anticipate 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). Mathematical models serve to explain complex biological phenomena and provide predictions that can be tested experimentally. They can provide plausible scenarios of a complex biological behaviour when intuition is not sufficient anymore.
The process of building these models needs to start with the analyses of omics data and the gathering of prior information (from published experiments and databases). A proper conception of these models might, in some cases, require the development of tools that facilitate not only the modelling of these biological problems, but also the analysis of the networks that control them. All these aspects are crucial for developing personalised mathematical models that can be used in the clinics as a tool for providing individual appropriate treatments for cancer patients.
A successful project is thus built on the three pillars: data analysis, mathematical modelling and tools’ development.