2 Syllabus: Population Genetics Simulation & Visualization
Course Number: AS.360.111
Schedule: Fall 2023, Tues 2:30 PM - 4:30 PM
Location Homewood Campus, UG Teaching Lab (UTL) 289
Student Hours: Wednesdays 5:30-6:30pm via Zoom; 15 minutes before/after class; by appointment, please email both instructors; Levi 200 or Zoom
Instructors:
Andrew Bortvin abortvi2@jh.edu
Sara Carioscia saracarioscia@jhu.edu
2.0.1 Course Description
Dive into the fascinating world of computational evolutionary biology with our course on “Population Genetics Simulation and Visualization.” This hands-on course is designed to equip students with the tools and knowledge needed to understand and analyze complex evolutionary dynamics using the SLiM (Selection, Linkage, and Mutation) simulation software. Through a blend of theoretical lectures and practical sessions, students will learn how to create and manipulate virtual populations, simulate genetic drift, natural selection, and other evolutionary forces. They will gain proficiency in setting up simulation scenarios, running experiments, and collecting raw data. Leveraging this data, students will explore various data visualization techniques to uncover patterns, trends, and insights in the simulated evolutionary processes. Prior programming experience is not required. Students from all departments and at all levels (including first-year undergraduates) are welcome.
2.0.2 Class Structure
Class Organization
Each class will be divided between lectures covering biological principles, live coding where we teach programming in SLiM, and in-class completion of assignments. As this is a longer class, we will take breaks between sections. Feel free to bring snacks or drinks.
Assignments
Each day, we will reserve time to work on in-class assignments, which will primarily focus on implementation of simulations and conceptual questions regarding the theory behind population genetics. Assigments will also include additional (optional) questions for students wishing to further develop their models.
Please submit all files via email to both instructors. Please save your .slim files as .txt (export and save as) and your resulting plots as .png files.
You are welcome to work together in small groups, and collaboration is encouraged. Likewise, we encourage you to seek answers online when encountering code or topics you’re unfamiliar with. However, please refrain from just copying someone else’s code – you should understand and be able to explain every line of code in your scripts.
2.0.3 Schedule
| Class Number | Date | Topics Covered |
|---|---|---|
| 1 | 10/31/23 | Intro to SLiM + molecular biology |
| 2 | 11/7/23 | Selective Sweeps |
| 3 | 11/14/23 | Multiple Populations |
| 4 | 11/21/23 | Reading in files and visualizing in R (virtual/asynch) |
| 5 | 11/28/23 | Splitting Populations |
| 6 | 12/5/23 | Visualizing local adaptation |
2.0.4 Grading
This course will be graded Satisfactory/Unsatisfactory, and individual assignments will be graded on reasonable completion (rather than accuracy of results). In each assignment, we will specify items to include in your submission; the assignment score will be the fraction of items completed. To achieve a Satisfactory, you must have an average of 70% completion across the assignments.
All assignments will be due at the end of the course period. However, please try to submit your work each week - we will look at your work in progress, provide written feedback, discuss any questions or opportunities for improvement, and let you know estimated percent completion.
2.0.5 Student Hours
We will host open student hours (time for students to chat about anything! Biology, coding, graduate school, materials directly or slightly less directly related to the course, actual assignments) through the end of the semester. In addition to scheduled times above, please email both instructors to schedule a time for either in-person or Zoom.