Probability and Biological Evolution
June 15 -19, 2015
June 15 -19, 2015
The overarching goal of this conference is to reflect the current methodological and conceptual advances in the study of stochastic processes used in modeling for ecology, population genetics and evolution. This is intended to stimulate the development of cutting-edge stochastic models that will promote a better understanding of evolutionary processes at various scales, ranging from genes to populations, communities and ecosystems. The main mathematical problems to be addressed can be described by the interrelated topics:
- Large-scale behavior and rare events in population dynamics
- Trees, coalescents and historical processes
- Generalized branching processes
- Spatial models in ecology and population genetics
- Random networks in epidemiology.
The conference shall favor the understanding of several important biological phenomena in fields such as epidemiology, ecology, population genetics and phylogenetics, especially large scale phenomena such as long-range colonizations, macro-evolution of life-history traits, species trees, and epidemics of emerging diseases. The study of such phenomena raises a number of deep questions which involve complex mathematical structures and require the introduction of new models and techniques in probability theory. A major aim of the conference is thus to highlight the study of advanced mathematical tools while bearing in mind the underlying biological reality and the potential applications in life sciences.
The interactions of genetics and ecology are of fundamental importance and still deserve more attention on the timescales of population genetics. The dependence of the individual fitness in reproduction upon the state of the entire population, and the interactions with (local and global) environmental conditions are crucial modeling ingredients. Additional topics which will be addressed in the conference, and which belong to biological evolution in a wider sense, are tumor growth models and probabilistic models from mathematical epidemiology that rely on tools from population dynamics and random graphs, and also relate to the population genetics of viruses.
Matthias Birkner (Johannes Gutenberg University of Mainz)
Alison Etheridge (University of Oxford)
Steve Evans (University of Berkeley)
Amaury Lambert (UPMC Paris 6)
Sylvie Méléard (Ecole polytechnique)
John Wakeley (Harvard University )
Etienne Pardoux (Aix-Marseille Université)
Anton Wakolbinger (Goethe University of Frankfurt)