Network Biology Methods and Applications
While extremely large datasets describing gene sequences, mRNA transcripts, protein abundance, and metabolite concentrations are increasingly commonplace, these represent only starting ‘parts lists’ that are usually insufficient to unlock mechanistic insi
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Molecular Biology™
Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK
For further volumes: http://www.springer.com/series/7651
Network Biology Methods and Applications Edited by
Gerard Cagney Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
Andrew Emili Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada and Department of Molecular Genetics and Microbiology, University of Toronto, Toronto, ON, Canada
Editors Gerard Cagney, Ph.D. Conway Institute of Biomolecular and Biomedical Research University College Dublin Dublin, Ireland [email protected]
Andrew Emili, Ph.D. Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research University of Toronto, Toronto ON, Canada and Department of Molecular Genetics and Microbiology, University of Toronto, Toronto ON, Canada [email protected]
ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-61779-275-5 e-ISBN 978-1-61779-276-2 DOI 10.1007/978-1-61779-276-2 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011934479 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)
Preface Researchers are all familiar with the spectacular success of reductionist approaches for elucidating biological mechanisms over the last century or so. Nevertheless, recent technological advances, particularly among the leading sub-specialities of molecular biology, present challenges to reductionism on at least two fronts. First, the explosive growth in the scale of data generated by modern, highly parallel, computerized, and increasingly automated lab methods has become a major impediment. For instance, next-generation sequencing technology is capable of producing terabytes of sequence information per day. It is also now mainstream to report global expression patterns for thousands of genes in multiple strains or cell lines subjected to different conditions. Similarly, phenotypic screens analyzing singly or multiply gene-disrupted cells and organisms have recently become promin
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