Teaching

INTRODUCTION TO SYNTHETIC BIOLOGY – BMME 420

Synthetic biology is a relatively new discipline with the goal of engineering biological systems with specific, designed functions. Example application areas include sensors, medical diagnostics, bioenergy/biofuels, programmable biomaterials, chemical synthesis, environmental testing, and many others. Synthetic biology is different from historical “genetic engineering” approaches in that a design engineering methodology is a central component of this discipline, as is the creation and use of standardized, well-characterized interchangeable parts. While oriented towards the creation of biological devices, advancements in the field will also provide significant insight into the core principles of biological systems. This course provides an introduction to both the theoretical principles as well as hands-on techniques of synthetic biology. It consists of a weekly lecture covering relevant background and theory as well as a lab focused on learning fundamental experimental techniques.

Principles of Network Pharmacology - BMME 521

This course is designed to introduce senior undergraduate and first-year graduate students from biomedical engineering, computational biology, and related disciplines to the interdisciplinary field of network pharmacology. During this course students will explore the integration of pharmacology, network theory, and computational biology to understand the complex interactions within biological systems and their implications for drug discovery and development. The course covers foundational principles, computational tools, and methodologies essential for analyzing and interpreting biological networks, drug-target interactions, disease modules, and personalized medicine approaches. Topics should also be highly relevant to those interested in drug delivery and regenerative medicine/tissue engineering. Through lectures, hands-on projects, and case studies, students will gain insights into the current trends, challenges, and future directions in network pharmacology.