Difference between revisions of "Evolution of polymorphism in plants"
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:[[Media:Polymorphism.zip]], [[Media:dated_tree.zip]], [[Media:frogs_bio10.zip]] | :[[Media:Polymorphism.zip]], [[Media:dated_tree.zip]], [[Media:frogs_bio10.zip]] | ||
'''Report: Project Summary & Code explanations''' | '''Report: Project Summary & Code explanations''' | ||
− | :[[Media:Report_BrownianMotion.pdf]] | + | :[[Media:Report_BrownianMotion.pdf]] [[Media:Report_Hoffman.pdf]] |
'''References:''' | '''References:''' | ||
:A Butler, A A King 2004 "Phylogenetic comparative analysis: A modeling approach for adaptive evolution" American Naturalist: 164(6): 683-695 | :A Butler, A A King 2004 "Phylogenetic comparative analysis: A modeling approach for adaptive evolution" American Naturalist: 164(6): 683-695 | ||
Back to [[UNIL BSc course: "Solving Biological Problems that require Math 2012"]] | Back to [[UNIL BSc course: "Solving Biological Problems that require Math 2012"]] |
Revision as of 09:45, 2 June 2012
Evolution of polymorphism in plants
Background:
- Understanding modes of species evolution is the major questions to the current evolutionary biology. As more DNA data become available, an increasing number of researchers is now switching to phylogeny-based stochastic models. Therefore, the key challenge today is to develop and test algorithms which can adequately describe evolution of phenotypes.
Goal:
- The goal of this project is to develop MCMC optimization of Ornstein-Uhlenbeck process with group-specific variance and then use it in phylogenetic comparative analysis to test for signal of directional/divergent selection in a group of plants
Mathematical tools:
- Statistics (stochastic models and MCMC) and programming. The students will learn how to use R to implement stochastic models and develop optimization procedures of the model parameters
Biological or Medical aspects:
- This kind of analysis allow to estimate the most probable way of evolution, and permit to answer a lot of question like phenotypic evolution, comparative analysis between species and more other.
Supervisors:
- Anna Kostikova & Nicolas Salamin
Students:
- Rémy Morier-Genoud
Presentation on Brownian Motion:
Implementation of Brownian Motion in Python: Code & Input examples:
Report: Project Summary & Code explanations
References:
- A Butler, A A King 2004 "Phylogenetic comparative analysis: A modeling approach for adaptive evolution" American Naturalist: 164(6): 683-695
Back to UNIL BSc course: "Solving Biological Problems that require Math 2012"