Summary: Course Announcement
MATH 605 Stochastic methods for biology
Time & Pace: MWF, 1:20 PM - 2:10 PM in Van Vleck (room TBD).
Instructor: David Anderson
Course Summary: Stochastic (that is, probabilistic) models have a long history in Biology. How-
ever, their use has greatly increased in recent years as advances in experimental methods in biology,
such as Green Fluorescent protein and laser traps, have enabled quantitative measurements at the
single cell, and even single molecule, level. Such experiments show time and time again that the
dynamics at this level are intrinsically stochastic, or "noisy." The (mathematical) implication of
this observation is that the standard deterministic models for dynamics (for example, ODE models)
need to sometimes be replaced with analogous stochastic models.
In addition to cellular processes, population, epidemic, and birth-death processes (among oth-
ers) can all be modeled stochastically. You can also use stochastic models to better understand
evolution via models for genetic mutation.
This course will focus on stochastic models of biological phenomena. We will cover the requi-
site probability to understand them (although, see the prerequisites below), and will learn different
methods of analysis: from the theoretical to the computational. By the end of this course you will
understand how stochastic models arise naturally in biology, and also be able to analyze and apply
computational techniques (such as the well known "Gillespie" or stochastic simulation algorithm)