Physical Models Of Living Systems

This textbook helps students develop many of the competencies that form the basis of the MCAT2015. The only prerequisite for this is first-year physics. With the more advanced "Track-2" sections at the end of each chapter, the book can be used in graduate-level courses as well. Written for intermediate-level undergraduates pursuing any science or engineering major, Physical Models of Living Systems, gives students the research skills they require but are often left unaddressed in traditional courses. Skills such as basic modeling, probabilistic modeling and data analysis 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. This living systems include virus dynamics, bacterial genetics, statistical inference and synthetic biology.

An overview of current models of biological systems, reflecting the major advances that have been made over the past decade.

Interactions between the fields of physics and biology reach back over a century, and some of the most significant developments in biology--from the discovery of DNA's structure to imaging of the human brain--have involved collaboration across this disciplinary boundary. For a new generation of physicists, the phenomena of life pose exciting challenges to physics itself, and biophysics has emerged as an important subfield of this discipline. Here, William Bialek provides the first graduate-level introduction to biophysics aimed at physics students. Bialek begins by exploring how photon counting in vision offers important lessons about the opportunities for quantitative, physics-style experiments on diverse biological phenomena. He draws from these lessons three general physical principles--the importance of noise, the need to understand the extraordinary performance of living systems without appealing to finely tuned parameters, and the critical role of the representation and flow of information in the business of life. Bialek then applies these principles to a broad range of phenomena, including the control of gene expression, perception and memory, protein folding, the mechanics of the inner ear, the dynamics of biochemical reactions, and pattern formation in developing embryos. Featuring numerous problems and exercises throughout, Biophysics emphasizes the unifying power of abstract physical principles to motivate new and novel experiments on biological systems. Covers a range of biological phenomena from the physicist's perspective Features 200 problems Draws on statistical mechanics, quantum mechanics, and related mathematical concepts Includes an annotated bibliography and detailed appendixes Instructor's manual (available only to teachers)

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

What is light? -- Photons and life -- Color vision -- How photons know where to go -- Optical phenomena and life -- Direct image formation -- Imaging as inference -- Imaging by X-ray diffraction -- Vision in dim light -- The mechanism of visual transduction -- The first synapse and beyond -- Electrons, photons, and the Feynman principle -- Field quantization, polarization, and the orientation of a single molecule -- Quantum-mechanical theory of FRET

In this book, physics in its many aspects (thermodynamics, mechanics, electricity, fluid dynamics) is the guiding light on a fascinating journey through biological systems, providing ideas, examples and stimulating reflections for undergraduate physics, chemistry and life-science students, as well as for anyone interested in the frontiers between physics and biology. Rather than introducing a lot of new information, it encourages young students to use their recently acquired knowledge to start seeing the physics behind the biology. As an undergraduate textbook in introductory biophysics, it includes the necessary background and tools, including exercises and appendices, to form a progressive course. In this case, the chapters can be used in the order proposed, possibly split between two semesters. The book is also an absorbing read for researchers in the life sciences who wish to refresh or go deeper into the physics concepts gleaned in their early years of scientific training. Less physics-oriented readers might want to skip the first chapter, as well as all the "gray boxes" containing the more formal developments, and create their own á-la-carte menu of chapters.

This book develops the mathematical tools essential for students in the life sciences to describe interacting systems and predict their behavior. From predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. Complex feedback relations and counter-intuitive responses are common in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science?

Award-winning prof brings you from first-year classes to the frontiers of systems and synthetic biology, epidemic modeling, and imaging. Physical Models of Living Systems first develops the frameworks needed to understand modern ideas about inference from data, as they relate to biological physics research. Later chapters develop stochastic simulation as a tool to handle more complex systems, and then dynamical systems theory applied to cellular control networks, both natural and synthetic. Along the way, you'll also see the foundations of revolutionary advances in imaging (superresolution and cryo-electron microscopy), along with epidemic modeling, mechanobiology, excitable media, and more. The text also has significant overlap with competencies covered in the MCAT exam. Dozens of problems at all levels, many of them new in this edition, will help you to gain simulation and data visualization skills useful in any branch of quantitative science research.