Stories of mathematics and computational science in genetic mapping

Eric Stone (ANU)

10-May-2022, 06:00-07:00 (23 months ago)

Abstract: Traits of interest often vary within a population, leading biologists to investigate the genetic basis of that observed variation. This can be done directly via an association study, in which one of many methods is used to identify correlational patterns that link genetic variation to trait variation. Alternatively, in experimental systems, individuals can be selectively bred to create a “genetic mapping population” with a more desirable signal-to-noise ratio. In this talk, I will share my experience creating mapping populations as a vehicle to introducing some of the mathematical and computational challenges that have ensued. I will discuss combinational and probabilistic issues that arise in ideal populations, contrasted by some algorithmic concerns that arise in natural populations. My goal is to provide a sampling of accessible problems in mathematics and computational science encountered in a practical biological context.

computational biologycomputational engineering, finance, and sciencenumerical analysiscomputational physics

Audience: researchers in the topic


ANU Mathematics and Computational Sciences Seminar

Organizers: Matthew Hole, Quanling Deng*
*contact for this listing

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