- Doctoral student in multidisciplinary research: Physiopathology and mathematical data processing :
Main scientific field: Applied Mathematics – Deep Learning Data Sciences
Secondary scientific field: Pathophysiology, Regenerative Medicine, Aging
Description of the subject :
The ultimate goal of the overall project is to build a 3D numerical simulation model to identify, mimic and study the self-organizing endogenous mechanisms of post-traumatic or aging tissue reconstruction in adult mammals. The types of self-organization prevalent in an equilibrium adult tissue and in a tissue under construction are
similar to the different phases of a physical system. Built on a functional, integrative and synthetic vision, the project will rely on the combination of rule-based agent-type heuristic models with sophisticated imaging techniques. The models made as user-friendly as possible will be used to define a list of key determinants involved in triggering phase transitions and reaching a new state of equilibrium. Biological experiments will be conducted in the laboratory to calibrate the models and to test, validate and improve the underlying model assumptions using state-of-the-art methodologies, particularly in imaging and image processing. The validated hypotheses will be pharmacological targets likely to allow complete tissue regeneration or reversion of tissue degeneration that occurs during aging.
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