Explore common mathematical frameworks for adaptation at different scales and link them with biological reality. The course is in a "flipped learning" style; each week, students read a book chapter and experiment with sample codes before the class.
In the first class of the week, they present what they have learned and raise questions.
In the second class of the week, they 1) present a paper in the reference list, 2) solve exercise problem(s), 3) make a new exercise problem and solve it, or 4) propose revisions in the chapter.
Toward the end of the course, students work on individual or group projects by picking any of the methods introduced in the course and apply that to a problem of their interest.
Assumes good knowledge of Python, statistics, and an ability to look at biological problems in a mathematical way.
OIST courses to complete beforehand: B31Statistical Tests and/or B32 Statistical Modeling