Opening conference of the semester

The opening conference of the semester will take place on Monday 18th of September 2017 at 6 p.m at the Grand Auditorium de l'Université Paul Sabatier (main building).

Schedule

Titles and abstracts of the talks

Manon Costa (Institut de Mathématiques de Toulouse, Université Paul Sabatier)

Some (stochastic) models to understand natural selection

Introduced by Darwin in the nineteenth century, the idea of ​​natural selection (the temporal evolution of species), is now commonly used in scientific reasoning. In mathematics many models have been developed to understand how the variability of individual traits, the role of mutations and the hazard in reproductions can combine to ensure the evolution of species? In this talk, I will present some examples of mathematical objects that allow us to predict the future of a population (its extinction, its diversity ...) or on the contrary to know its biological past (the common ancestor of a group for example), then I will give examples of current research on probabilistic modeling of biodiversity.

Pierre Cordelier (Centre de Recherches en Cancérologie de Toulouse (CRCT), INSERM)

Simulation model of virus infection in pancreatic cancer

Solid tumors such as pancreatic cancer cause hundreds of thousands of deaths each year. In this dismal context, scientists have developed novel therapeutic weapons such as Tumor killing viruses (oncolytic viruses) that prove efficient in experimental models of cancer, but only few studies have yet successfully concluded late-phase or pivotal clinical studies. Modelling biological events involving oncolytic viruses is currently gaining attention and might lead to a better understanding of the complex interactions between cancer cells and these cutting-edge, candidate therapeutics. This lecture will first describe how oncolytic virus infect and spread into tumors, from the biologist point of view. Next, we will discuss about the current efforts to generate models so that they fit as well as possible to biological data, to provide significant biological insight into the field of virus dynamics. Last, we will highlight some of the challenges that need to be overcome in order to build mathematical and computation models that are clinically predictive.