Fitness landscapes, genetic interactions, and inexact data
In this talk I will present a mathematical approach (no background in mathematics will be necessary) to fitness landscapes. The approach is applicable to any (one-‐dimensional) quantitative measure (phenotype) associated to genotypes, not necessarily fitness. The approach allows to characterise fitness landscapes in terms of their "shapes" and opens up a rich toolbox of (statistical) algebraic geometry available to analyse the landscapes.
A fitness landscape carries comprehensive information about the genetic system with respect to the phenotype under consideration. In particular, genetic interaction, including higher-‐order, are encoded into the landscape.
However, due to the imprecise nature of fitness (phenotype) measurements, statistical uncertainty makes fitness landscape inference a challenging problem.
Remarkably, several characteristics of fitness landscape, including higher‐order genetic interactions, can be robustly learned in highly uncertain scenarios.
I will demonstrate how this result can be used in practice, without any knowledge of mathematics.
|Date||Tuesday, 24 April 2018|
|Time||12:00pm - 1:00pm|
|Event Category||Health Sciences|
|Location||Biochemistry seminar rm 231|