Methods and Models in Mathematical Biology Deterministic and Stochas
This book developed from classes in mathematical biology taught by the authors over several years at the Technische Universität München. The main themes are modeling principles, mathematical principles for the analysis of these models, and model-based ana
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Johannes Müller Christina Kuttler
Methods and Models in Mathematical Biology Deterministic and Stochastic Approaches
Lecture Notes on Mathematical Modelling in the Life Sciences
Series Editors Angela Stevens Michael C. Mackey
More information about this series at http://www.springer.com/series/10049
Johannes MRuller • Christina Kuttler
Methods and Models in Mathematical Biology Deterministic and Stochastic Approaches
123
Christina Kuttler Centre for Mathematical Sciences Technical University Munich Garching, Germany
Johannes MRuller Centre for Mathematical Sciences Technical University Munich Garching, Germany
ISSN 2193-4789 ISSN 2193-4797 (electronic) Lecture Notes on Mathematical Modelling in the Life Sciences ISBN 978-3-642-27250-9 ISBN 978-3-642-27251-6 (eBook) DOI 10.1007/978-3-642-27251-6 Library of Congress Control Number: 2015945816 Springer Heidelberg New York Dordrecht London © Springer-Verlag Berlin Heidelberg 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer-Verlag GmbH (www.springer.com)
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Foreword
Mathematical biology is in the moment a prosperous and exciting field. New experimental methods require more and refined models. While experimental data of traditional experiments often reflect an (population) average (like an average protein level), more and more sophisticated experiments are able to characterise single objects like cells, sometimes even single proteins. We begin to understand that populations are heterogeneous, that, e.g. even bacteria form a complex world of interacting individuals. This new quality in data and perception forms challenges that are to be met and, correspondingly, a new quality in models is required. We need to think about the classical approaches: Where are classical models still an appropriate tool? Where do we need to extend them? Where are completely new ideas necessary? To meet these challenges, a master cla
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