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 R
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