Author: Leonid Burstein

Primary MATLAB® for Life Sciences: Guide for Beginners

eBook: US $39 Special Offer (PDF + Printed Copy): US $151
Printed Copy: US $131
Library License: US $156
ISBN: 978-1-60805-805-1 (Print)
ISBN: 978-1-60805-804-4 (Online)
Year of Publication: 2013
DOI: 10.2174/97816080580441130101

Introduction

This e-book provides readers a short introductory MATLAB® course oriented towards various collaborative areas of biotechnology and bioscience. The text concentrates on MATLAB® fundamentals and gives examples of its application to various problems in computational biology, molecular biology, biokinetics, biomedicine, bioinformatics, and biotechnology. MATLAB® is presented with examples and applications to various school-level and advanced life science / bioengineering problems - from growing populations of microorganisms and population dynamics, reaction kinetics and reagent concentrations, predator-prey models, to data fitting and time series analysis.

The book is divided into 7 chapters containing material carefully selected and tailored to teaching several groups of biotechnology students. The topics are presented in a manner that allows readers to proceed sequentially on the strength of the preceding material.

Primary MATLAB® for Life Sciences: A Guide for Beginners is essentially a concise and comprehensive text that provides an easy grasp and to-the-point access to the MATLAB® tool to the community of life sciences and bioengneering undergraduates and specialists.

Preface

The last two-three decades are a remarkable, revolutionary time, in which computer- and life sciences have been contributing much to one another. Computer engineers are actively working in bio-areas while life science specialists are working on bio-computers, cell lasers, and other amazing devices recently introduced into the non-biological domain. Therefore, today is a real need to familiarize biotechnologists with the computing software used by ‘classical’ engineers. The present book is a short introductory MATLAB® guide addressed to a wide life science audience - undergraduate and graduate students and practicing engineers. It provides the MATLAB® fundamentals with a variety of application examples and problems from current biotechnology, computational biology, molecular biology, bio-kinetics, biomedicine, ecology, population dynamics, and bioengineering. I hope that many non-programmer students, engineers and scientists from this area will find the software user-friendly and extremely convenient in solving their specific problems.

The book was planned at a time when its predecessor “MATLAB® in bioscience and biotechnology” (L. Burstein, Biohealthcare Publishing (Oxford) Limited, Oxford-New York, 2011, pp. 230) was still in production and thus unavailable. Accordingly, their sections 2, dealing with MATLAB® basics, are similar. Otherwise, the book is tailored to the level of a newcomer in computer calculations, and contains topics and examples not given in the predecessor. Most of the problems required in the latter to be solved by the reader, are reproduced with their solutions – and vice versa. The used sample data and at least 80% of the problems were revised or completely reformulated in this book, which contains a new chapter on useful tools such as the Basic Fitting and Time Series interfaces.

The book accumulates the experience of many years of MATLAB teaching in introductory and advanced courses for students, engineers and scientists specializing in the area in question.

I hope this book will prove useful to students and engineers in both the natural and life sciences and enable them to work with one of the finest software tools.

Leonid Burstein
Kinneret College on the Sea of Galilee
School of Engineering
Quality Assurance Department
M.P. Jordan Valley, 15132
Israel
E-mail: leonidburstein@gmail.com

RELATED BOOKS

.Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms.
.Arduino and SCILAB based Projects.
.Arduino meets MATLAB: Interfacing, Programs and Simulink.
.Budget Optimization and Allocation: An Evolutionary Computing Based Model.