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: Dynamics of Viruses and Their Microbial Hosts (Princeton).

• PrefaceQuantitative biosciences at all scales of lifeThe goalThe structure of this bookYou can do itAcknowledgmentsI Molecular and Cellular Biosciences1 Fluctuations and the Nature of Mutations1.1 Chance favors the independent mutation1.2 Cellular phenotypes1.3 Mutations that depend on selection1.4 Independent mutations: A continuous model1.5 Modeling the growth of (discrete) mutants1.6 Variance of mutants when mutations are independent of selection1.7 On (in)direct inference1.8 Take-home messages1.9 Homework problems1.10 Technical appendix2 Bistability of Genetic Circuits2.1 More is different2.2 Molecular cast and scene2.3 The first ingredient: Regulation of a target gene2.4 Feedback and bistability—autoregulation2.5 The dynamics of a genetic toggle switch2.6 Take-home messages2.7 Homework problems2.8 Technical appendix3 Stochastic Gene Expression and Cellular Variability3.1 Living with randomness3.2 Stochasticity in gene regulation3.3 Characterizing dynamics of individual cells, given stochastic gene expression3.4 Is gene expression bursty?3.5 The geometry of bursts3.6 Take-home messages3.7 Homework problems3.8 Technical appendix4 Evolutionary Dynamics: Mutations, Selection, and Diversity4.1 Evolution in action4.2 Selection and the disappearance of diversity4.3 Mechanisms that restore diversity4.4 Stochasticity in the evolution of populations—baseline expectations4.5 Evolutionary dynamics with stochasticity and selection4.6 Sweeps or hitchhiking or both?4.7 Take-home messages4.8 Homework problems4.9 Technical appendixII Organismal Behavior and Physiology5 Robust Sensing and Chemotaxis5.1 On taxis5.2 Why swim?5.3 The behavior of swimming E. coli5.4 Chemotaxis machinery5.5 Signaling cascades5.6 Fine-tuned adaptation5.7 Buffering and robust cellular adaptation5.8 Take-home messages5.9 Homework problems5.10 Technical appendix6 Nonlinear Dynamics and Signal Processing in Neurons6.1 Walking in the path of Hodgkin and Huxley6.2 The brain: Memory, learning and behavior6.3 Of ions and neurons6.4 Dynamical properties of excitable neuronal systems6.5 From neurons to neural networks and information processing6.6 Take-home messages6.7 Homework problems6.8 Technical appendix7 Excitations and Signaling from Cells to Tissue7.1 From excitable cells to excitable systems7.2 Principles of oscillatory dynamics7.3 Relaxation oscillations—a generalized view7.4 Principles of excitability: From cardiac cells to tissue7.5 Take-home messages7.6 Homework problems7.7 Technical appendix8 Organismal Locomotion through Water, Air, and Earth8.1 Movement from within8.2 Movement with brief moments in air8.3 Principles of slow swimming8.4 Terrestrial locomotion8.5 Take-home messages8.6 Homework problems8.7 Technical appendixIII Populations and Ecological Communities9 Flocking and Collective Behavior: When Many Become One9.1 Life is with other organisms9.2 Endogenous vs. exogenous drivers of spatial ordering9.3 Vicsek model: Uniting static and dynamic order9.4 Collective decision making at the flock scale9.5 Take-home messages9.6 Homework problems9.7 Technical appendix10 Conflict and Cooperation Among Individuals and Populations10.1 Games, relatively speaking10.2 Payoffs: A classic approach10.3 From payoffs to populations10.4 Games that real organisms play10.5 Feedback between strategies and the environment10.6 Take-home messages10.7 Homework problems10.8 Technical appendix11 Eco-evolutionary Dynamics11.1 The power of exponentials11.2 Canonical models of population dynamics11.3 Predator-prey dynamics11.4 Toward predator-prey dynamics with rapid evolution11.5 Take-home messages11.6 Homework problems11.7 Technical appendix12 Outbreak Dynamics: From Prediction to Control12.1 Modeling in the age of pandemics12.2 The core model of an outbreak: The SIR model12.3 The Shape of an outbreak12.4 Principles of control12.5 EVD: A case study in control given uncertainty12.6 On the ongoing control of SARS-CoV-212.7 Take-home messages12.8 Homework problems12.9 Technical appendixIV The Future of Ecosystems13 Ecosystems: Chaos, Tipping Points, and Catastrophes13.1 Ecosystems—the integrated frontier13.2 Chaos in communities13.3 Condorcet and catastrophes13.4 Thresholds in ecosystems and the Earth system13.5 The challenge continuesBibliographyIndexColor plates follow page