Stiochiometries, propensities, and ODE models: tutorial videos from the Undergraduate Quantitative Biology Summer School - King, Connor, instructor; Munsky, Brian, instructor; May, Michael, author; Cook, Joshua, author; Munsky, Brian, author (Colorado State University. Libraries, 2025)
The field of quantitative biology (q-bio) seeks to provide precise and testable explanations for observed biological phenomena by applying mathematical and computational methods. The central goals of q-bio are to (1) systematically propose quantitative hypotheses in the form of mathematical models, (2) demonstrate that these models faithfully capture a specific essence of a biological process, and (3) correctly forecast the dynamics of the process in new, and previously untested circumstances. Achieving these goals depends on accurate analysis and incorporating informative experimental data to constrain the set of potential mathematical representations. In this introductory tutorial, we provide an overview of the state of the field and introduce some of the computational methods most commonly used in q-bio. In particular, we examine experimental techniques in single-cell imaging, computational tools to process images and extract quantitative data, various mechanistic modeling approaches used to reproduce these quantitative data, and techniques for data-driven model inference and model-driven experiment design. All topics are presented in the context of additional online resources, including open-source Python notebooks and open-ended practice problems that comprise the technical content of the annual Undergraduate Quantitative Biology Summer School (UQ-Bio).
Tutorial videos from the Undergraduate Quantitative Biology Summer School.
The videos in this repository are provided to accompany the following manuscript I(accepted for publication in Physical Biology): Luis U. Aguilera, Lisa M. Weber, Eric Ron, Connor R. King, Kaan Ocal, Alex Popinga, Joshua Cook, Michael P. May, William S. Raymond, Zachary R. Fox, Linda S. Forero-Quintero, Jack R. Forman, Alexandre David, Brian Munsky, "Methods in Quantitative Biology from Analysis of Single-Cell Microscopy Images to Inference of Predictive Models for Stochastic Gene Expression," Physical Biology (in press), 2025.; LUA, LMW, CRK, AP, LSFQ, AD, and BM are in the Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA.; ER, JC, MPM, WSR, JRF, AD and BM are in the School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USA.; CRK is in the Cell and Molecular Biology Program, Colorado State University, Fort Collins, CO 80523, USA.; KO is in the School of BioSciences, University of Melbourne, Parkville, Victoria 3010, AU.; AP is in the School of Biological Sciences, University of Auckland, Auckland CBD, Auckland 1010, NZ.; ZRF is at Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA.
To request a transcript, please contact library_digitaladmin@mail.colostate.edu or call (970) 491-1844.
Keywords: stiochiometries; propensity functions; ODE model; single-cell processing; uq-BIO