Some Mathematical Models from Population Genetics [electronic resource] : École d'Été de Probabilités de Saint-Flour XXXIX-2009 /
Etheridge, Alison.author.
Ecole d'été de probabilités de Saint-Flour.
This work reflects sixteen hours of lectures delivered by the author at the 2009 St Flour summer school in probability. It provides a rapid introduction to a range of mathematical models that have their origins in theoretical population genetics. The models fall into two classes: forwards in time models for the evolution of frequencies of different genetic types in a population; and backwards in time (coalescent) models that trace out the genealogical relationships between individuals in a sample from the population. Some, like the classical Wright-Fisher model, date right back to the origins of the subject. Others, like the multiple merger coalescents or the spatial Lambda-Fleming-Viot process are much more recent. All share a rich mathematical structure. Biological terms are explained, the models are carefully motivated and tools for their study are presented systematically.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
This work reflects sixteen hours of lectures delivered by the author at the 2009 St Flour summer school in probability. It provides a rapid introduction to a range of mathematical models that have their origins in theoretical population genetics. The models fall into two classes: forwards in time models for the evolution of frequencies of different genetic types in a population; and backwards in time (coalescent) models that trace out the genealogical relationships between individuals in a sample from the population. Some, like the classical Wright-Fisher model, date right back to the origins of the subject. Others, like the multiple merger coalescents or the spatial Lambda-Fleming-Viot process are much more recent. All share a rich mathematical structure. Biological terms are explained, the models are carefully motivated and tools for their study are presented systematically.
Biomathematics.
Mathematical models.
Partial differential equations.
Statistics .
Genetics and Population Dynamics.
Mathematical and Computational Biology.
Mathematical Modeling and Industrial Mathematics.
Partial Differential Equations.
Statistics for Life Sciences, Medicine, Health Sciences.
Lecture notes in mathematics