Award-winning professor brings you from first-year physics and chemistry to the frontier of single-molecule biophysics. Biological Physics is a university textbook that focuses on results in molecular motors, self-assembly, and single-molecule manipulation that have revolutionized the field in recent years, and integrates these topics with classic results in statistical physics, biophysical chemistry, and neuroscience. The text also provides foundational material for the emerging fields of nanotechnology and mechanobiology, and has significant overlap with the revised MCAT exam. This inexpensive new edition updates the classic book, particularly the chapter on motors, and incorporates many clarifications and enhancements throughout. Exercises are given at all levels of difficulty. Instead of offering a huge pile of facts, the discovery-style exposition frequently asks the reader to reflect on "How could anything like that happen at all?" and then shows how science, and scientists, have proceeded incrementally to peel back the layers of mystery surrounding these beautiful mechanisms. Working through this book will give you an appreciation for how science has advanced in the past, and the skills and frameworks needed to push forward in the future. Additional topics include the statistical physics of diffusion; bacterial motility; self-assembly; entropic forces; enzyme kinetics; ion channels and pumps; the chemiosmotic mechanism and its role in ATP maintenance; and the discovery of the mechanism of neural signaling.

Biological Physics Student Edition: Energy, Information, Life

Science & Math

Nelson, Philip

Astronomy & Space Science

Chiliagon Science

A Student's Guide to Python for Physical Modeling

Python is a computer programming language that is rapidly gaining popularity throughout the sciences. 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 tutorial focuses on fundamentals and introduces a wide range of useful techniques, including:
<ul>
<li>Basic Python programming and scripting</li>
<li>Numerical arrays</li>
<li>Two- and three-dimensional graphics</li>
<li>Monte Carlo simulations</li>
<li>Numerical methods, including solving ordinary differential equations</li>
<li>Image processing</li>
<li>Animation</li>
</ul>Numerous code samples and exercises―with solutions―illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more.

Computers & Technology

Kinder, Jesse M.

Computer Science

Princeton University Press

From Photon to Neuron: Light, Imaging, Vision

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. 
<ul>
<li>Assumes familiarity with first-year undergraduate physics and the corresponding math</li>
 <li>Overlaps the goals of the MCAT, which now includes data-based and statistical reasoning</li>
 <li>Advanced chapters and sections also make the book suitable for graduate courses</li>
 <li>An Instructor's Guide and illustration package is available to professors</li>
</ul>

Biological Sciences

Politics & Social Sciences

Anthropology

A Student's Guide to Python for Physical Modeling: Updated Edition

A fully updated tutorial on the basics of the Python programming language for science students
Python is a computer programming language that is rapidly gaining popularity throughout the sciences. This fully updated 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 tutorial focuses on fundamentals and introduces a wide range of useful techniques, including:
<ul>
<li>Basic Python programming and scripting 
</li>
<li>Numerical arrays 
</li>
<li>Two- and three-dimensional graphics 
</li>
<li>Monte Carlo simulations 
</li>
<li>Numerical methods, including solving ordinary differential equations 
</li>
<li>Image processing 
</li>
<li>Animation 
</li>
</ul>Numerous code samples and exercises--with solutions―illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more. This current edition brings the discussion of the Python language, Spyder development environment, and Anaconda distribution up to date. In addition, a new appendix introduces Jupyter notebooks.

Reference

A fully updated tutorial on the basics of the Python programming language for science students
Python is a computer programming language that is rapidly gaining popularity throughout the sciences. This fully updated 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 tutorial focuses on fundamentals and introduces a wide range of useful techniques, including:
<ul>
<li>Basic Python programming and scripting</li>
<li>Numerical arrays</li>
<li>Two- and three-dimensional graphics</li>
<li>Monte Carlo simulations</li>
<li>Numerical methods, including solving ordinary differential equations</li>
<li>Image processing</li>
<li>Animation</li>
</ul>Numerous code samples and exercises--with solutions―illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more. This current edition brings the discussion of the Python language, Spyder development environment, and Anaconda distribution up to date. In addition, a new appendix introduces Jupyter notebooks.

A Student's Guide to Python for Physical Modeling: Second Edition

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: <ul> <li>Basic Python programming and scripting</li> <li>Numerical arrays</li> <li>Two- and three-dimensional graphics</li> <li>Animation</li> <li>Monte Carlo simulations</li> <li>Numerical methods, including solving ordinary differential equations</li> <li>Image processing</li> </ul> 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.

Product Description 
A fully updated tutorial on the basics of the Python programming language for science studentsPython 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:\nBasic Python programming and scripting 
Numerical arrays 
Two- and three-dimensional graphics 
Animation 
Monte Carlo simulations 
Numerical methods, including solving ordinary differential equations 
Image processing\nNumerous 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. 
 Review 
Praise for the previous edition"At a brief 160 pages, it is quite possible for a motivated student to complete it in just a few sittings. . . . A fine introduction." ―American Journal of Physics\n"The text serves as an excellent stepping stone into the world of using Python in computational science for undergraduate students with a strong background in mathematics." 
― 
Kevin Thielen and Vivienne Tien, Computing in Science & Engineering\n"Kinder and Nelson's engaging introduction to scientific programming in Python is careful and thorough, and focuses on actual essentials. Bread-and-butter concepts and techniques, belonging in every computational scientist's toolbox, are presented with well-thought-out examples drawn from daily research practice. This is a clever text, inviting students to take that most important step: to dive right in and start coding." 
―Cornelis Storm, Eindhoven University of Technology\n"Kinder and Nelson have written a friendly and succinct, yet surprisingly comprehensive, introduction to scientific programming in Python. It's written not just for computational scientists, but for anyone who needs to plot and analyze experimental data, numerically solve equations, or learn the basics of programming. Even students who have experience in programming will appreciate the thought-provoking exercises and guidelines for getting the most out of Python." 
―Vinothan N. Manoharan, Harvard University\n"This book is tailor-made for physical scientists beginning to do computation. More than in any other programming book I've read, the authors are conscientious―they anticipate and troubleshoot the areas of confusion readers might encounter. Kinder and Nelson carefully and effectively guide readers toward the goal of formulating a computational problem and solving it." 
―Justin Bois, California Institute of Technology\n"Like patient driving instructors, Kinder and Nelson guide the hands of novice programming students from the get-go, helping them to avoid obstacles and crashes. By the end of the book, students should be racing around confidently like pros, using Python to solve scientific problems of data analysis, modeling, and visualization. A great textbook for a first course in modern scientific programming in any context, and one that I'll be using myself." 
―Garnet Kin-Lic Chan, Princeton University\n"This book covers the basics of Python programming language, with an emphasis on physical modeling. It provides a very useful introduction to Python for

Integrating new results which have revolutionized the field of biological physics over the past few years with classic results, Biological Physics is an ideal text for undergrads and graduate students alike.

Biological Physics: with New Art by David Goodsell

W.H.Freeman

See the best price to sell, buy, or rent books by Nelson, Philip. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Nelson, Philip

See the best price to sell, buy, or rent books by Nelson, Philip. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Biological Physics Student Edition: Energy, Information, Life

A Student's Guide to Python for Physical Modeling

A Student's Guide to Python for Physical Modeling

From Photon to Neuron: Light, Imaging, Vision

From Photon to Neuron: Light, Imaging, Vision

A Student's Guide to Python for Physical Modeling: Updated Edition

A Student's Guide to Python for Physical Modeling: Updated Edition

A Student's Guide to Python for Physical Modeling: Second Edition

A Student's Guide to Python for Physical Modeling: Second Edition

Biological Physics: with New Art by David Goodsell