AI-ASSISTED COMPUTATIONAL BIOLOGY MODELING

Credit Hours: 3 · Graduate

This course introduces the fundamentals of computational modeling for biological and biomedical systems. For each topic, you will learn the necessary biological background and discover how these core principles can be translated into mathematical models. You will explore cornerstone models in biology, including biochemical kinetics, neural activity, and epidemic dynamics. Using a modern, AI-augmented workflow in Python, this course is accessible to students with little to no coding experience. You will learn to leverage AI as a collaborative partner to perform statistical analysis, create data visualizations, and simulate complex biological systems using ODEs, PDEs, and agent-based models. Recommended Prerequisite(s): Basic knowledge of life sciences (e.g., biochemistry, cell biology), linear algebra, and ordinary differential equations is expected. Some knowledge of Python programming is recommended, but this course focuses on the application of AI-assisted coding to solve problems in biomedical research and is not a traditional introductory programming course.