"I will talk about tumor growth modelling in xenograft experiments. It's a statistical approach (see e.g. the ref. below), but I'd like to incorporate more mathematical modelling." Roy Choudhury, K., Kasman, I., Plowman, G., 2010, Analysis of multi-arm tumor growth trials in xenograft animals using phase change adaptive piecewise quadratic models, Statistics in Medicine, 29, 2399-2409

Living systems are characterized by variability; they are subject to constant evolution through the three processes of population growth, selection and mutations, a principle established by C. Darwin. In a very simple, general and idealized description, their environment can be considered as a nutrient shared by all the population. This allows certain individuals, characterized by a 'phenotypical trait', to expand faster because they are better adapted to use the environment. This leads to select the'fittest trait' in the population (singular point of the system). On the other hand, the new-born individuals undergo small variations of the trait under the effect of genetic mutations. In these circumstances, is it possible to describe the dynamical evolution of the current trait? An area of population biology that aims at describing mathematically these processes is born in the 1980's under the name of 'adaptive dynamics' and, compared to population genetics, considers usually asexual reproduction, a continuous phenotypical trait and population growth. We will give a self-contained mathematical model of such dynamics, based on parabolic equations, and show that an asymptotic method allows us to formalize precisely the concepts of monomorphic or polymorphic population. Then, we can describe the evolution of the 'fittest trait' and eventually to compute various forms of branching points which represent the cohabitation of two different populations. The concepts are based on the asymptotic analysis of the above mentioned parabolic equations once appropriately rescaled. This leads to concentrations of the solutions and the difficulty is to evaluate the weight and position of the moving Dirac masses that describe the population. We will show that a new type of Hamilton-Jacobi equation, with constraints, naturally describes this asymptotic. Some additional theoretical questions as uniqueness for the limiting H.-J. equation will also be addressed. Several other modeling methods have been proposed, stochastic individual based models, evolutionary game theory, dynamical systems. Connections will be presented too. This talk is based on collaborations with G. Barles, J. Carrillo, S. Cuadrado, O. Diekmann, M. Gauduchon, S. Genieys, P.-E. Jabin, S. Mirahimmi, S. Mischler and P. E. Souganidis.

Biology is a tremendously diverse field covering systems operating at vastly different scales, with differing levels of interaction between these. Much of the effort in mathematical biology has been driven by the desire to develop the general principles by which biological systems are organized and operate. Yet at the same time there are demands for answers to quite specific questions to better manage natural systems, to enhance human health, and to plan for the future impacts of human actions. I will give a variety of examples of projects in mathematical ecology that lie at the interface between theory and practice, providing some indication of the utility of quantitative methods to elucidate general patterns of natural system response to management actions. These will include applications of optimal control methods to problems in wildlife management and disease ecology as well as a discussion of individual-based models. An objective of these approaches is to develop hypothetical "best" methods to manage a system, and use this as a template to compare and contrast management scenarios arising from the differing view points of diverse stakeholders in a relative assessment framework.