APHYS 237/BIO251: Quantitative evolutionary dynamics and genomics

The genomics revolution has fueled a renewed push to model evolutionary processes in quantitative terms. This course will provide an introduction to quantitative evolutionary modeling through the lens of statistical physics. Topics will range from the foundations of theoretical population genetics to experimental evolution of laboratory microbes. Course work will involve a mixture of pencil-and-paper math, writing basic computer simulations, and downloading and manipulating DNA sequence data from published datasets. This course is intended for upper level physics and math students with no biology background, as well as biology students who are comfortable with differential equations and probability.


Winter 2020

Course Meeting Schedule: TTh 10:30am – 11:50am
Course Meeting Location: Clark Center S361
Instructor: Prof. Benjamin Good, Office: Clark S231A, Email: bhgood@stanford.edu
Office Hours: Th 11:50am-1pm or by appointment.

Syllabus
Mathematical Background
Notes on approximating Gaussians
Final Project Instructions

Problem Sets

Data files for problem sets

Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4

Lecture Notes

Lecture 1 (Overview slides, Math Background, Bio Background)
Lecture 2 (PDF)
Lecture 3 (PDF)
Lecture 4 & 5 (PDF)
Lecture 6 (PDF)
Lecture 8 (Heuristic Approach, Mutations)
Lecture 9 (PDF)
Lecture 10 (PDF)
Lecture 11 (PDF)
Lecture 12 (PDF)
Lecture 14 (PDF)
Lecture 15 (PDF)
Lecture 16 (PDF)
Lecture 17 (PDF)
Lecture 18 (PDF)

Other materials

1. BH Good (2016), Molecular evolution in rapidly evolving populations, Chapter 1
2. SF Levy, JR Blundell, et al (Nature 2015), Quantitative evolutionary dynamics using high-resolution lineage tracking, Supplementary Information
3. DS Fisher (Les Houches Course 11, 2007), Evolutionary dynamics
4. Korolev et al (Rev Mod Phys, 2010), Genetic demixing and evolution in linear stepping stone models
5. Neher and Shraiman (Rev Mod Phys, 2011), Statistical genetics and evolution of quantitative traits