MATH 428/528

Spring 2017

Stochastic Elements in Mathematical Biology



Instructor: Yevgeniy Kovchegov
e-mail: kovchegy @math. oregonstate.edu
Office: Kidder 368C
Office Phone No: 7-1379
Office Hours: M 1:30pm - 3:00pm, W 1:00pm - 2:30pm



Place and time: MWF 10:00am to 10:50am, room BEXL 102.

Web materials:
Charles M. Grinstead and J. Laurie Snell, Introduction to Probability available as a FREE e-book at http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf

Course description: This course is an introduction to stochastic modeling of biological processes. Stochastic models covered may include Markov processes in both continuous and discrete time, urn models, branching processes, and coalescent processes. Biological applications modeled may include genetic drift, population dynamics, genealogy, demography, and epidemiology. Mathematical results will be qualitatively interpreted and applied to the biological process under investigation.

The course will cover the following topics:

A variety of mathematical techniques will be covered when analyzing these models.

Syllabus:  PDF

Homework: TBA



Schedule:
Monday, April 3  Review of probability. Conditional probability. Bayes’ Theorem. Lecture 1 slides (PDF)
Wednesday, April 5  Review of probability. Conditional probability. Bayes’ Theorem. Independent events. Lecture 2 slides (PDF)
Friday, April 7  Review of probability. Bayes’ Theorem. Independent events. Examples. Lecture 3 slides (PDF)
Monday, April 10  Review of combinatorics. Permutations and combinations. Generalized combinations. Binomial theorem. Lecture 4 slides (PDF)
Wednesday, April 12  Introduction to random variables. Binomial random variable. Expectation of a random variable. Wright-Fisher Model. Lecture 5 slides (PDF)
Friday, April 14  Binomial random variable. Expectation of a random variable. Poisson random variable. Geometric random variables. Variance and standard deviation. Lecture 6 slides (PDF)
Monday, April 17  Variance and standard deviation of discrete random variables. Markov and Chebyshev inequalities. Lecture 7 slides (PDF)
Wednesday, April 19  Introduction into Markov chains. Wright-Fisher model as a Markov chain. Birth-and-death processes. Moran process. Lectures 8-11 slides (PDF)
Friday, April 21  Birth-and-death processes. Moran process. Lectures 8-11 slides (PDF)