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Titles By Philip Nelson
Intended for intermediate-level undergraduates in any science or engineering major, the only prerequisite for this course is first-year physics. Supplementary sections make the book also suitable as the basis of a graduate-level course. This low-cost second edition expands the first one with four new chapters as well as adding add many clarifications and updates. Dozens of exercises are included at all levels of complexity, many involving computer work. Throughout, the goal is for you to gain the fluency needed to derive every result for yourself.
Along the way, you will acquire several research skills that are often not addressed in traditional courses:
- Basic modeling skills, including dimensional analysis, identification of variables, and ODE formulation;
- Probabilistic modeling skills, including stochastic simulation;
- Data analysis methods, including maximum likelihood and Bayesian methods;
- Computer programming using a general-purpose platform like MATLAB or Python, with short codes written from scratch;
- Dynamical systems, particularly feedback control, with phase portrait methods.
All of these basic skills, which are relevant to nearly any field of science or engineering, are presented in the context of case studies from living systems, including:
- Virus dynamics;
- Bacterial genetics and evolution of drug resistance;
- Statistical inference;
- Superresolution microscopy and cryo-electron microscopy;
- Stochastic simulation, for example of gene expression;
- Synthetic biology;
- Epidemic modeling;
- Naturally evolved cellular control circuits, including homeostasis, genetic switches, and the mitotic clock;
- Excitable media.
A fully updated tutorial on the basics of the Python programming language for science students
Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed.
This guide introduces a wide range of useful tools, including:
- Basic Python programming and scripting
- Numerical arrays
- Two- and three-dimensional graphics
- Monte Carlo simulations
- Numerical methods, including solving ordinary differential equations
- Image processing
Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.
A richly illustrated undergraduate textbook on the physics and biology of light
Students in the physical and life sciences, and in engineering, need to know about the physics and biology of light. Recently, it has become increasingly clear that an understanding of the quantum nature of light is essential, both for the latest imaging technologies and to advance our knowledge of fundamental life processes, such as photosynthesis and human vision. From Photon to Neuron provides undergraduates with an accessible introduction to the physics of light and offers a unified view of a broad range of optical and biological phenomena. Along the way, this richly illustrated textbook builds the necessary background in neuroscience, photochemistry, and other disciplines, with applications to optogenetics, superresolution microscopy, the single-photon response of individual photoreceptor cells, and more.
With its integrated approach, From Photon to Neuron can be used as the basis for interdisciplinary courses in physics, biophysics, sensory neuroscience, biophotonics, bioengineering, or nanotechnology. The goal is always for students to gain the fluency needed to derive every result for themselves, so the book includes a wealth of exercises, including many that guide students to create computer-based solutions. Supplementary online materials include real experimental data to use with the exercises.
- Assumes familiarity with first-year undergraduate physics and the corresponding math
- Overlaps the goals of the MCAT, which now includes data-based and statistical reasoning
- Advanced chapters and sections also make the book suitable for graduate courses
- An Instructor's Guide and illustration package is available to professors