Probabilistic methods for pathogen and copy number evolution

Job Candidate Talk
Tuesday, January 24, 2017 - 10:00
1 hour (actually 50 minutes)
Skiles 005
UC Berkeley
Biology is becoming increasingly quantitative, with large genomic datasets being curated at a rapid rate. Sound mathematical modeling as well as data science approaches are both needed to take advantage of these newly available datasets. I will describe two projects that span these approaches. The first is a Markov chain model of naturalselection acting at two scales, motivated by the virulence-transmission tradeoff from pathogen evolution. This stochastic model, under a natural scaling, converges to a nonlinear deterministic system for which we can analytically derive steady-state behavior. This analysis, along with simulations, leads to general properties of selection at two scales. The second project is a bioinformatics pipeline that identifies gene copy number variants, currently a difficult problem in modern genomics. This quantificationof copy number variation in turn generates new mathematical questionsthat require the type of probabilistic modelling used in the first project.