CMSC 858M: Computational Evolutionary Dynamics
About this course
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 cs.umd.edu; 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
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.
This syllabus may change as the course itself evolves.
- Week 1: What is evolution? Replication,
Mutation, Selection. How can we model it computationally? Chapter 1 of
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?
Week 3: Network Archeology: How can past
configurations of a network be computationally inferred?
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?
Week 5: BattleGraph! And introduction to evolutionary game theory.
Week 6: Evolution of evolvability (and battlegraph results)
Week 7: Turmites and spatial games (parts
of Chapter 9 in Evolutionary Dynamics.
Week 8: Idealized models of evolving
systems: flocks, swarms. When does coordination emerge?
Week 9: Simplified model of evolution:
context-free grammars for strings, graphs, and images.
Week 10: Genetic mixing: horizontal gene
transfer, recombination, reassortment, and transposons.
Week 11: Open or catch up day (topic to
be decided later)
Week 12: Open or catch up day (topic to
be decided later).
Week 13: Project
- Lecture Notes
- Reading: Nowak, ch. 1 and ch. 2
- Homework: choose 5 papers from the list below you would like to present.
Initially Created Oct 29, 2010. Images taken from Navlakha &