Course Number: 02-731
Some of the most serious public health problems we face today, from drug-resistant bacteria, to cancer, all arise from a fundamental property of living systems—their ability to evolve. Since Darwin’s theory of natural selection was first proposed, we have begun to understand how heritable differences in reproductive success drive the adaptation of living systems. This makes it intuitive and tempting to view evolution from an optimization perspective. However, genetic drift, phenotypic trade-offs, constraints, and changing environments, are among the many factors that may limit the optimizing force of natural selection. This tug-of-war between selection and drift, between the forces that produce variation in a population, and the forces suppressing this variation, make evolutionary processes much more complex to model and understand than previously thought.
The aim of this class is to provide an introduction into the theoretical formalism necessary to understand how biological systems are shaped by the forces and constraints driving evolutionary dynamics. I will introduce population genetic theory as a lens for the understanding and interpretation of modern datasets, such as datasets of human world-wide genomic and epigenomic variation or tumor genomic heterogeneity. By the end of the course, you should have learned to build evolutionary models, as well as the basic differences between idealized models and the data you might encounter in real life. The class is group-project based and you will work together to explore open questions in evolution.
This course is intended for students who want to better understand the theory of evolution and evolutionary strategies.
Prerequisite(s): (15112) and (21241) and (36225 or 36218 or 21325 or 15259)
Learning ObjectivesBy the end of the course, you should have learned to build evolutionary models, as well as the basic differences between idealized models and the data you might encounter in real life.
The class is group-project based and you will work together to explore open questions in evolution.