Computational Methods for Biological Modeling and Simulation
Course Number: 02-512
This course covers a variety of computational methods important for modeling and simulation of biological systems. It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems.
The course will emphasize practical algorithms and algorithm design methods drawn from various disciplines of computer science and applied mathematics that are useful in biological applications. The general topics covered will be models for optimization problems, simulation and sampling, and parameter tuning. Course work will include problem sets with significant programming components and independent or group final projects.
Key Topics:
- Modeling and simulation of biological systems
- Models for optimization problems
- Simulation and sampling
- Parameter tuning
Academic Year: 2026-2027
Semester(s): Fall
Required/Elective: Required
Units: 9
Prerequisite(s): 15-110 or 02-201 or 15-112
Learning Objectives
- Learn how to formally define and reason about mathematical models of biological systems.
- Be familiar with a collection of commonly encountered discrete optimization problems, both tractable and intractable, and be able to reason about adapting them to related problems and solving them in practice.
- Be familiar with continuous optimization problems and know general techniques for solving them in constrained and unconstrained variants.
- Understand Markov models, how to pose them, how to analyze their mixing times, and how to use both discrete and continuous time variants.
- Know how to sample from a probability density.
- Know how to pose and numerically integrate ordinary, partial, and stochastic differential equations.
- Know how to pose parameter inference problems as optimization or sampling problems and be familiar with a set of general techniques for solving them.
- Understand basic principles behind validating models and quantifying uncertainty in those models.
Assessment Structure:
- Problem sets with significant programming components
- Individual or group final projects