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"This guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students-whether from the life sciences, physics, computational sciences, engineering, or mathematics-how to reason quantitatively in the face of uncertainty"--
About the author
Joshua S. Weitz is professor and the Clark Leadership Chair in Data Analytics in the Department of Biology at the University of Maryland. Previously, he held the Tom and Marie Patton Chair in Biological Sciences at the Georgia Institute of Technology, where he founded the Interdisciplinary Graduate Program in Quantitative Biosciences. He is the author of
Quantitative Viral Ecology (Princeton).
Marian Domínguez-Mirazo is a PhD candidate in Quantitative Biosciences in the School of Biological Sciences at the Georgia Institute of Technology.
Summary
A hands-on lab guide in the R programming language that enables students in the life sciences to reason quantitatively about living systems across scales
This lab guide accompanies the textbook Quantitative Biosciences, providing students with the skills they need to translate biological principles and mathematical concepts into computational models of living systems. This hands-on guide uses a case study approach organized around central questions in the life sciences, introducing landmark advances in the field while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively in the face of uncertainty.
- Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communities
- Encourages good coding practices, clear and understandable modeling, and accessible presentation of results
- Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale
- Builds practical expertise in a range of methods, including sampling from probability distributions, stochastic branching processes, continuous time modeling, Markov chains, bifurcation analysis, partial differential equations, and agent-based simulations
- Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own
- Stand-alone computational lab guides for Quantitative Biosciences also available in Python and MATLAB
