Course Outlines: 

The course will be a modern introduction to statistical physics with biological examples. We will discuss random walks, partial differential equations, markov processes, random matrix theory, entropy and information, Ising model and its applications in Biology and end with a discussion of Scaling and Universality. We will consider biological applications across scales including molecular, cell and population biology.

Course Outcomes:

At the end of the course, the students will be familiar with stochastic processes and how to model them, with examples from population biology. They will have an appreciation for how to model gene regulation, both deterministic and stochastic. They will understand the close relationship between entropy and information theory, and see the application of information theory in various systems including chemotaxis, kinetic proofreading, bacterial persisters and positional information during development. Finally, they will get an appreciation for scaling, universality and emergence in statistical physics. They will have a mathematical understanding of stochastic differential equations, markov processes, maximum entropy, random matrix theory, and scaling analysis.