This course explores how living systems operate—from the scale of single cells to entire populations—through the application of quantitative principles. Students will learn to connect biological mechanisms with computational models and experimental data. By the end of this class, students should be able to:
- Understand quantitative principles of how living systems work; Approaches for building and evaluating models of biological processes;
- Explore key concepts in practice through computational modules in Python, R, and MATLAB.
The course is designed to help students from physics, engineering, mathematics, computing, and the life sciences translate classroom learning into practical research skills, bridging disciplines to tackle complex problems in modern biology.
University of Maryland, Spring 2026 (GEMS297)
Gemstone students carry out interdisciplinary research with the guidance of a faculty mentor. The team develops its website, prepares and presents its research proposal and begins its research project.
University of Maryland, Winter 2026 (BSCI338V)
Fundamentals of coding using Python with a focus on applications in the life sciences. Explore how programming is used in the broader context of research in the life sciences. Students will learn fundamental coding and apply their coding knowledge through student-developed projects. Prior coding experience is not necessary.
Infectious disease dynamics: a systems approach
Understanding and controlling the dynamics of infectious diseases remains challenging since factors that drive the dynamics are highly interrelated, ranging from host-pathogen interactions, the impact of the environment, and human factors like communication, individual behavior, and surveillance. In this course, we will introduce systems thinking as a tool to characterize interaction networks that impact population-level disease dynamics. We will discuss their use for building strategies of disease prevention and mitigation for a range of diseases in human and environmental contexts, and learn to develop and run systems-informed epidemiological simulations.
Foundations in Quantitative Biosciences
Georgia Institute of Technology, Fall 2016/17/18/19/20
The class is organized around understanding key advances in the biosciences, one organizing unit at a time, in which the advances depended critically on quantitative methods and reasoning. Both foundational advances and recent challenges will be discussed. Each week, students will be exposed to:
- Methods for developing and analyzing quantitative models;
- Logic for how to reason given uncertainty in the biosciences;
- Computational skills to implement and support a thorough understanding of stochastic and dynamic modeling at the interface between mathematical formalism and biological data.
The overall objective of the course is to train graduate students how to reason quantitatively in the biosciences given uncertainty in mechanisms, rates and reliability of measurements.