CMSC 858M: Computational Evolutionary Dynamics

About this course

The organizing question of this course is: how can we model how systems are evolving over time, and how can we use those models to computationally predict future behavior and to "predict" unobserved past behavior. Our main focus will be on applications to biological systems including viruses, other genomes, and --- most prominently --- the evolution of biological networks. We will also consider applications in non-biological areas.

Instructor: Carl Kingsford; carlk AT; Office hours: by appointment in CBCB 3113.

Meeting Time: Fridays, 9:00-11:30.

Prerequisites: No previous biological knowledge is assumed. A certain amount of mathematical maturity is required (that any CS Ph.D. student will likely have), but the class will generally be self-contained.

Grading: There will be several homework sets, one or two in-class presentations per person, a take-home final, and a class project.

Textbook: We will cover several chapters in the textbook Evolutionary Dynamics by Martin Nowak. Much additional material will come from recent papers.

Credit: This course is not a core Ph.D. or M.S. course, and it does not count towards MS Comps. This is a lecture / seminar course about recent research in the field.

Tentative syllabus

This syllabus may change as the course itself evolves.

  1. Week 1: What is evolution? Replication, Mutation, Selection. How can we model it computationally? Chapter 1 of Evolutionary Dynamics.
  2. Week 2: Models for the evolution of changing networks. How can the evolution of biological (and social) networks be simulated? How do different evolutionary models lead to qualitatively different networks?
  3. Week 3: Network Archeology: How can past configurations of a network be computationally inferred?
  4. Week 4: Evolution of modularity in biological networks. What processes lead to the emergence of "modules" or reusable components in evolving systems? How does modularity emerge without design?
  5. Week 5: BattleGraph! And introduction to evolutionary game theory.
  6. Week 6: Evolution of evolvability (and battlegraph results)
  7. Week 7: Turmites and spatial games (parts of Chapter 9 in Evolutionary Dynamics.
  8. Week 8: Idealized models of evolving systems: flocks, swarms. When does coordination emerge?
  9. Week 9: Simplified model of evolution: context-free grammars for strings, graphs, and images.
  10. Week 10: Genetic mixing: horizontal gene transfer, recombination, reassortment, and transposons.
  11. Week 11: Open or catch up day (topic to be decided later)
  12. Week 12: Open or catch up day (topic to be decided later).
  13. Week 13: Project Presentations.

Related Reading

See here

Initially Created Oct 29, 2010. Images taken from Navlakha & Kingsford.