Bøger af Joshua S. Weitz

A hands-on lab guide in the MATLAB 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 communitiesEncourages good coding practices, clear and understandable modeling, and accessible presentation of resultsHelps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scaleBuilds 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 simulationsBridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their ownStand-alone computational lab guides for Quantitative Biosciences also available in Python and R

A hands-on lab guide in the Python 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 communitiesEncourages good coding practices, clear and understandable modeling, and accessible presentation of resultsHelps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scaleBuilds 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 simulationsBridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their ownStand-alone computational lab guides for Quantitative Biosciences also available in R and MATLAB

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 communitiesEncourages good coding practices, clear and understandable modeling, and accessible presentation of resultsHelps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scaleBuilds 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 simulationsBridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their ownStand-alone computational lab guides for Quantitative Biosciences also available in Python and MATLAB

When we think about viruses we tend to consider ones that afflict humans-such as those that cause influenza, HIV, and Ebola. Yet, vastly more viruses infect single-celled microbes. Diverse and abundant, microbes and the viruses that infect them are found in oceans, lakes, plants, soil, and animal-associated microbiomes. Taking a vital look at the "e;microscopic"e; mode of disease dynamics, Quantitative Viral Ecology establishes a theoretical foundation from which to model and predict the ecological and evolutionary dynamics that result from the interaction between viruses and their microbial hosts.Joshua Weitz addresses three major questions: What are viruses of microbes and what do they do to their hosts? How do interactions of a single virus-host pair affect the number and traits of hosts and virus populations? How do virus-host dynamics emerge in natural environments when interactions take place between many viruses and many hosts? Emphasizing how theory and models can provide answers, Weitz offers a cohesive framework for tackling new challenges in the study of viruses and microbes and how they are connected to ecological processes-from the laboratory to the Earth system.Quantitative Viral Ecology is an innovative exploration of the influence of viruses in our complex natural world.