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Quantitative Biosciences Companion in MATLAB

Quantitative Biosciences Companion in MATLAB
Author: Joshua S. Weitz
Publisher: Princeton University Press
Total Pages: 257
Release: 2024-01-09
Genre: Science
ISBN: 0691259623
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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 communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds 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 simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in Python and R

Quantitative Biosciences Companion in Python

Quantitative Biosciences Companion in Python
Author: Joshua S. Weitz
Publisher: Princeton University Press
Total Pages: 273
Release: 2024-01-09
Genre: Science
ISBN: 0691259615
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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 communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds 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 simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in R and MATLAB

Quantitative Biosciences Companion in R

Quantitative Biosciences Companion in R
Author: Joshua S. Weitz
Publisher: Princeton University Press
Total Pages: 272
Release: 2024-03-05
Genre: Computers
ISBN: 0691255660
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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 communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds 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 simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in Python and MATLAB

Quantitative Biosciences

Quantitative Biosciences
Author: Joshua S. Weitz
Publisher: Princeton University Press
Total Pages: 409
Release: 2024-01-09
Genre: Science
ISBN: 0691256489
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A hands-on approach to quantitative reasoning in the life sciences Quantitative Biosciences establishes the quantitative principles of how living systems work across scales, drawing on classic and modern discoveries to present a case study approach that links mechanisms, models, and measurements. Each case study is organized around a central question in the life sciences: Are mutations dependent on selection? How do cells respond to fluctuating signals in the environment? How do organisms move in flocks given local sensing? How does the size of an epidemic depend on its initial speed of spread? Each question provides the basis for introducing landmark advances in the life sciences while teaching students—whether from the life sciences, physics, computational sciences, engineering, or mathematics—how to reason quantitatively about living systems given uncertainty. Draws on real-world case studies in molecular and cellular biosciences, organismal behavior and physiology, and populations and ecological communities Stand-alone lab guides available in Python, R, and MATLAB help students move from learning in the classroom to doing research in practice Homework exercises build on the lab guides, emphasizing computational model development and analysis rather than pencil-and-paper derivations Suitable for capstone undergraduate classes, foundational graduate classes, or as part of interdisciplinary courses for students from quantitative backgrounds Can be used as part of conventional, flipped, or hybrid instruction formats Additional materials available to instructors, including lesson plans and homework solutions

Quantitative Biosciences Companion in R

Quantitative Biosciences Companion in R
Author: Joshua S. Weitz
Publisher: Princeton University Press
Total Pages: 273
Release: 2024-01-09
Genre: Science
ISBN: 0691259607
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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 communities Encourages good coding practices, clear and understandable modeling, and accessible presentation of results Helps students to develop a diverse repertoire of simulation approaches, enabling them to model at the appropriate scale Builds 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 simulations Bridges the gap between the classroom and research discovery, helping students to think independently, troubleshoot and resolve problems, and embark on research of their own Stand-alone computational lab guides for Quantitative Biosciences also available in Python and MATLAB

Applied Mathematics for the Analysis of Biomedical Data

Applied Mathematics for the Analysis of Biomedical Data
Author: Peter J. Costa
Publisher: John Wiley & Sons
Total Pages: 408
Release: 2017-02-21
Genre: Mathematics
ISBN: 1119269512
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Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® toolbox for the collection, visualization, and evaluation of experimental and real-life data Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government–sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real–world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods to determine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing. In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book’s technical merit and value to industry professionals. Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code. In addition, the book features: Real–world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen Clear delineation of topics to accelerate access to data analysis Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.

So Simple a Beginning

So Simple a Beginning
Author: Raghuveer Parthasarathy
Publisher: Princeton University Press
Total Pages: 336
Release: 2022-02-08
Genre: Science
ISBN: 0691200408
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A biophysicist reveals the hidden unity behind nature’s breathtaking complexity The form and function of a sprinting cheetah are quite unlike those of a rooted tree. A human being is very different from a bacterium or a zebra. The living world is a realm of dazzling variety, yet a shared set of physical principles shapes the forms and behaviors of every creature in it. So Simple a Beginning shows how the emerging new science of biophysics is transforming our understanding of life on Earth and enabling potentially lifesaving but controversial technologies such as gene editing, artificial organ growth, and ecosystem engineering. Raghuveer Parthasarathy explains how four basic principles—self-assembly, regulatory circuits, predictable randomness, and scaling—shape the machinery of life on scales ranging from microscopic molecules to gigantic elephants. He describes how biophysics is helping to unlock the secrets of a host of natural phenomena, such as how your limbs know to form at the proper places, and why humans need lungs but ants do not. Parthasarathy explores how the cutting-edge biotechnologies of tomorrow could enable us to alter living things in ways both subtle and profound. Featuring dozens of original watercolors and drawings by the author, this sweeping tour of biophysics offers astonishing new perspectives on how the wonders of life can arise from so simple a beginning.

Introduction to MATLAB® for Biologists

Introduction to MATLAB® for Biologists
Author: Cerian Ruth Webb
Publisher: Springer
Total Pages: 175
Release: 2019-08-01
Genre: Science
ISBN: 3030213374
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This textbook takes you from the very first time you open MATLAB® through to a position where you can comfortably integrate this computer language into your research or studies. The book will familiarise you with the MATLAB interface, show you how to use the program ́s built-in functions and carefully guide you towards creating your own functions and scripts so that you can use MATLAB as a sophisticated tool to support your own research. A central aim of this book is to provide you with the core knowledge and skills required to become a confident MATLAB user so that you can find and make use of the many specialist functions and toolboxes that have been developed to support a wide range of biological applications. Examples presented within the book are selected to be relevant to biological scientists and they illustrate some of the many ways the program can be incorporated into, and used to enhance, your own research and studies. The textbook is a must-have for students and researchers in the biological sciences. It will also appeal to readers of all backgrounds who are looking for an introduction to MATLAB which is suitable for those with little or no experience of programming.

A Matlab Companion for Multivariable Calculus

A Matlab Companion for Multivariable Calculus
Author: Jeffery Cooper
Publisher: Academic Press
Total Pages: 312
Release: 2001
Genre: Computers
ISBN: 012187625X
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Offering a concise collection of MatLab programs and exercises to accompany a third semester course in multivariable calculus, A MatLab Companion for Multivariable Calculus introduces simple numerical procedures such as numerical differentiation, numerical integration and Newton's method in several variables, thereby allowing students to tackle realistic problems. The many examples show students how to use MatLab effectively and easily in many contexts. Numerous exercises in mathematics and applications areas are presented, graded from routine to more demanding projects requiring some programming. Matlab M-files are provided on the Harcourt/Academic Press web site at http://www.harcourt-ap.com/matlab.html. Computer-oriented material that complements the essential topics in multivariable calculus Main ideas presented with examples of computations and graphics displays using MATLAB Numerous examples of short code in the text, which can be modified for use with the exercises MATLAB files are used to implement graphics displays and contain a collection of mfiles which can serve as demos

A MatLab® Companion to Complex Variables

A MatLab® Companion to Complex Variables
Author: A. David Wunsch
Publisher: CRC Press
Total Pages:
Release: 2017-06-29
Genre:
ISBN: 9781138441651
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This book is intended for someone learning functions of a complex variable and who enjoys using MATLAB. It will enhance the exprience of learning complex variable theory and will strengthen the knowledge of someone already trained in ths branch of advanced calculus. ABET, the accrediting board for engineering programs, makes it clear that engineering graduates must be skilled in the art of programming in a language such as MATLAB. Supplying students with a bridge between the functions of complex variable theory and MATLAB, this supplemental text enables instructors to easily add a MATLAB component to their complex variables courses.A MATLAB Companion to Complex Variables provides readers with a clear understanding of the utility of MATLAB in complex variable calculus. An ideal adjunct to standard texts on the functions of complex variables, the book allows professors to quickly find and assign MATLAB programming problems that will strengthen students� knowledge of the language and concepts of complex variable theory.The book shows students how MATLAB can be a powerful learning aid in such staples of complex variable theory as conformal mapping, infinite series, contour integration, and Laplace and Fourier transforms. In addition to MATLAB programming problems, the text includes many examples in each chapter along with MATLAB code.Fractals, the most recent interesting topic involving complex variables, demands to be treated with a language such as MATLAB. This book concludes with a Coda, which is devoted entirely to this visually intriguing subject.MATLAB is not without constraints, limitations, irritations, and quirks, and there are subtleties involved in performing the calculus of complex variable theory with this language. Without knowledge of these subtleties, engineers or scientists attempting to use MATLAB for solutions of practical problems in complex variable theory suffer the risk of