Computational Systems Biology. From Molecular Mechanisms to Disease PDF
Preview Computational Systems Biology. From Molecular Mechanisms to Disease
COMPUTATIONAL SYSTEMS BIOLOGY SECOND EDITION COMPUTATIONAL SYSTEMS BIOLOGY SECOND EDITION Edited by Roland Eils andREs KRiEtE AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, UK 32 Jamestown Road, London, NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Copyright © 2014, 2006 Elsevier, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier. com/locate/permissions, and selecting Obtaining permission to use Elsevier material. Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made. Library of Congress Cataloging-in-Publication Data Computational systems biology (Kriete) Computational systems biology / edited by Andres Kriete, Roland Eils. -- Second edition. p. ; cm. Includes bibliographical references and indexes. ISBN 978-0-12-405926-9 (alk. paper) I. Kriete, Andres, editor of compilation. II. Eils, Roland, editor of compilation. III. Title. [DNLM: 1. Computational Biology. 2. Systems Biology. QU 26.5] QH324.2 570.1’13--dc23 2013045039 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. ISBN: 978-0-12-405926-9 For information on all Academic Press publications visit our website at store.elsevier.com Printed in the United States of America 14 15 10 9 8 7 6 5 4 3 2 1 Contributors Chapter 1 Frédéric Crémazy Department of Synthetic Roland Eils Division of Theoretical Bio Systems Biology and Nuclear Organization, informatics (B080), German Cancer Research Swammerdam Institute for Life Sciences, Center (DKFZ), Heidelberg, Germany University of Amsterdam, Amsterdam, The Department for Bioinformatics and Func Netherlands tional Genomics, Institute for Pharmacy Matteo Barberis Department of Synthetic Systems and Molecular Biotechnology (IPMB) Biology and Nuclear Organization, Swam and BioQuant, Heidelberg University, merdam Institute for Life Sciences, University Heidelberg, Germany of Amsterdam, Amsterdam, The Netherlands Andres Kriete School of Biomedical Engineering, Science and Health Systems, Drexel University, Chapter 4 Philadelphia, PA, USA Ursula Klingmüller, Marcel Schilling, Sonja Depner, Lorenza A. D’Alessandro Division Chapter 2 Systems Biology of Signal Transduction, Robert B. Russell, Gordana Apic, Olga Kalinina, German Cancer Research Center (DKFZ), Leonardo Trabuco, Matthew J. Betts, Qianhao Heidelberg, Germany Lu CellNetworks, University of Heidelberg, Heidelberg, Germany Chapter 5 Christina Kiel EMBL/CRG Systems Biology Chapter 3 Re search Unit, Centre for Genomic Regulation Hans V. Westerhoff Department of Synthetic (CRG), Barcelona, Spain Systems Biology and Nuclear Organization, Universitat Pompeu Fabra (UPF), Barcelona, Spain Swammerdam Institute for Life Sciences, Luis Serrano EMBL/CRG Systems Biology Re University of Amsterdam, Amsterdam, The search Unit, Centre for Genomic Regulation Netherlands (CRG), Barcelona, Spain Department of Molecular Cell Physiology, Faculty Universitat Pompeu Fabra (UPF), Barcelona, Spain of Earth and Life Sciences, VU University ICREA, Barcelona, Spain Amsterdam, The Netherlands Manchester Centre for Integrative Systems Bio Chapter 6 logy (MCISB), Manchester, UK Seiya Imoto Human Genome Center, Institute Fei He Manchester Centre for Integrative Systems of Medical Science, The University of Tokyo, Biology (MCISB), Manchester, UK Minatoku, Tokyo, Japan Department of Automatic Control and systems Hiroshi Matsuno Faculty of Science, Yamaguchi Engineering, The University of Sheffield, University, Yoshida, Yamaguchi, Japan Sheffield, UK Satoru Miyano Human Genome Center, Insti Ettore Murabito Manchester Centre for Integrative tute of Medical Science, The University of Systems Biology (MCISB), Manchester, UK Tokyo, Minatoku, Tokyo, Japan ix x CONTRIBUTORS Chapter 7 Chapter 11 Hong-Wu Ma Tianjin Institute of Industrial Bio Reinhard Laubenbacher Virginia Bioinformatics technology, Chinese Academy of Sciences, Institute, Virginia Tech, Blacksburg VA, USA Tianjin, P.R. China Pedro Mendes Virginia Bioinformatics Institute, School of Informatics, University of Edinburgh, Virginia Tech, Blacksburg VA, USA Edinburgh, UK School of Computer Science, The University of An-Ping Zeng Institute of Bioprocess and Bio Manchester, Manchester, UK systems Engineering, Hamburg University of Technology, Denickestrasse, Germany Chapter 12 Joseph Xu Zhou, Xiaojie Qiu, Aymeric Fouquier Chapter 8 d’Herouel, Sui Huang Institute for Systems Stanley Gu Department of Bioengineering, Uni Biology, Seattle, WA, USA versity of Washington, Seattle, WA, USA Herbert Sauro Department of Bioengineering, University of Washington, Seattle, WA, USA Chapter 13 John Cole, Mike J. Hallock, Piyush Labhsetwar, Chapter 9 Joseph R. Peterson, John E. Stone, Zaida Juergen Eils Division of Theoretical Bioinformat Luthey-Schulten University of Illinois at ics, German Cancer Research Center (DKFZ), UrbanaChampaign, USA Heidelberg, Germany Elena Herzog Division of Theoretical Bioinformat Chapter 14 ics, German Cancer Research Center (DKFZ), Jean-Luc Bouchot Department of Mathematics, Heidelberg, Germany Drexel University, PA, Philadelphia, USA Baerbel Felder Division of Theoretical Bioinforma tics, German Cancer Research Center (DKFZ), William L. Trimble Institute for Genomics and Heidelberg, Germany Systems Biology, Argonne National Laboratory, Department for Bioinf ormatics and Functional University of Chicago, Chicago, IL, USA Genomics, Institute for Pharmacy and Gregory Ditzler Department of Electrical and Molecular Biotechnology (IPMB) and BioQuant, Computer Engineering, Drexel University, Heidelberg University, Heidelberg, Germany PA, Philadelphia, USA Christian Lawerenz Division of Theoretical Bio Yemin Lan School of Biomedical Engineering, informatics, German Cancer Research Center Science and Health, Drexel University, PA, (DKFZ), Heidelberg, Germany Philadelphia, USA Roland Eils Division of Theoretical Bioinformat Steve Essinger Department of Electrical and Com ics, German Cancer Research Center (DKFZ), puter Engineering, Drexel University, PA, Heidelberg, Germany Philadelphia, USA Department for Bioinformatics and Functional Gail Rosen Department of Electrical and Com Genomics, Institute for Pharmacy and Molec puter Engineering, Drexel University, PA, ular Biotechnology (IPMB) and BioQuant, Philadelphia, USA Heidelberg University, Heidelberg, Germany Chapter 10 Chapter 15 Jean-Christophe Leloup, Didier Gonze, Albert Helder I Nakaya Department of Pathology, Goldbeter Unité de Chronobiologie théorique, Emory University, Atlanta, GA, USA Faculté des Sciences, Université Libre de Bru Vaccine Research Center, Emory University, xelles, Campus Plaine, Brussels, Belgium Atlanta, GA, USA x xi CONTRIBUTORS Department of Clinical Analyses and Toxicology, Chapter 18 University of Sao Paulo, Sao Paulo, SP, Brazil Hang Chang, Gerald V. Fontenay, Cemal Bilgin, Bahram Parvin Life Sciences Division, Law rence Berkeley National Laboratory, Berkeley, Chapter 16 CA, USA Julien Delile Institut des Systèmes Complexes Alexander Borowsky Center for Comparative Paris IledeFrance (ISCPIF), CNRS, Paris, Medicine, University of California, Davis, CA, France USA. Neurobiology and Development Lab, Terrasse, Paul Spellman Department of Biomedical Engi GifsurYvette Cedex, France neering, Oregon Health Sciences Univers ity, Portland, Oregon, USA René Doursat Institut des Systèmes Com plexes Paris IledeFrance (ISCPIF), Chapter 19 CNRS, Paris, France Stefan M. Kallenberger Department for Bio School of Biomedical Engineering, Drexel Uni informatics and Functional Genomics, Divi versity, Philadelphia, PA, USA sion of Theoretical Bioinformatics, German Nadine Peyriéras Neurobiology and Develop Cancer Research Center (DKFZ), Institute for ment Lab, Terrasse, GifsurYvette Cedex, Pharmacy and Molecular Biotechnology France (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Stefan Legewie Institute of Molecular Biology, Chapter 17 Mainz, Germany Andres Kriete School of Biomedical Engineering, Roland Eils Department for Bioinformatics and Science and Health Systems, Drexel Univer Functional Genomics, Division of Theoretical sity, Bossone Research Center, Philadelphia, Bioinformatics, German Cancer Research PA, USA Center (DKFZ), Institute for Pharmacy and Mathieu Cloutier GERAD and Department of Molecular Biotechnology (IPMB) and Bio Chemical Engineering, Ecole Polytechnique Quant, Heidelberg University, Heidelberg, de Montreal, Montreal, QC, Canada Germany xi Preface Computational systems biology, a term coined in this area. If compared to the first edition by Kitano in 2002, is a field that aims at a published in 2005, the second edition has been system-level understanding by modeling and specifically extended to reflect new frontiers of analyzing biological data using computation. systems biology, including modeling of whole It is increasingly recognized that living s ystem cells, studies of embryonic development, the cannot be understood by studying individual immune systems, as well as aging and cancer. parts, while the list of molecular components As in the previous edition, basics of informa- in biology is ever growing, accelerated by tion and data integration technologies, genome sequencing and high-throughput standards, modeling of gene, signaling and omics techniques. Under the guiding vision of metabolic networks remain comprehensively systems biology, sophisticated computational covered. Contributions have been selected methods help to study the interconnection of and compiled to introduce the different meth- parts in order to unravel complex and net- ods, including methods dissecting biological worked biological phenomena, from protein complexity, modeling of dynamical proper- interactions, pathways, networks, to whole ties, and biocomputational perspectives. cells and multicellular complexes. Rather Beside the primary authors and their than performing experimental observations respective teams who have dedicated their alone, systems biology generates knowledge time to contribute to this book, the editors and understanding by entering a cycle of would like to thank numerous reviewers of model construction, quantitative simulations, individual chapters, but in particular Jan and experimental validation of model predic- Eufinger for support of the editorial work. tions, whereby a formal reasoning becomes It is often mentioned that biological sys- key. This requires a collaborative input of tems in its entirety present more than a sum experimental and theoretical biologists work- of its parts. To this extent, we hope that the ing together with system analysts, computer chapters selected for this book not only give scientists, mathematicians, bioengineers, a contemporary and comprehensive over- physicists, as well as physicians to contend look about the recent developments, but that creatively with the hierarchical and nonlinear this volume advances the field and encour- nature of cellular systems. ages new strategies, interdisciplinary coop- This book has a distinct focus on computa- eration, and research activities. tional and engineering methods related to sys- tems biology. As such, it presents a timely, multi-authored compendium representing Roland Eils and Andres Kriete state-of-the-art computational technologies, Heidelberg and Philadelphia, standards, concepts, and methods developed September 2013 xiii C H A P T E R 1 Introducing Computational Systems Biology Roland Eilsa,b, Andres Krietec aDivision of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Heidelberg, Germany bDepartment for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany cSchool of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA C O N T E N T S 1 Prologue 1 3 Outlook 6 2 Overview of the content 4 References 7 We need to turn data into knowledge and we need a framework to do so. S. Brenner, 2002. 1 PROLOGUE The multitude of the computational tools needed for systems biology r esearch can roughly be classified into two categories: system identification and behavior analysis (Kitano 2001). In molecular biology, system identification amounts to identifying the regulatory relation- ships between genes, proteins, and small molecules, as well as their inherent dynamics hid- den in the specific kinetic and binding parameters. System identification is arguably one of the most complicated problems in science. While behavior analysis is solely performed on a model, model construction is a process tightly connected to reality but part of an iterative process between data analysis, simulation, and experimental validation (Figure 1.1). A typical 1 Computational Systems Biology, Second Edition © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/B978-0-12-405926-9.00001-0 2 1. INTROduCINg COmPuTATIONAl SySTEmS BIOlOgy FIGURE 1.1 Key to systems biology is an iterative cycle of experimentation, model building, simulation and validation. modeling cycle begins with a reductionist approach, creating the simplest possible model. The modeling process generates an understanding of the underlying structures, and components are represented graphically with increasing level of formalization, until they can be converted into a mathematical representation. The minimal model then grows in complexity, driven by new hypotheses that may not have been apparent from the phenomenological descriptions. Then, an experiment is designed using the biological system to test whether the model predic- tions agree with the experimental observations of the system behavior. The constitutive model parameters may be measured directly or may be inferred during this validation process, how- ever, the propagation of errors through these parameters present significant challenges for the modeler. If data and predictions agree, a new experiment is designed and performed. This pro- cess continues until sufficient experimental evidence in favor of the model is collected. Once the system has been identified and a model constructed, the system behavior can be studied, for instance, by numerical integration or sensitivity analysis against external perturbations. Although the iterative process is well defined, the amount of data to be merged into this process can be immense. The human genome project is one of the hallmarks indicating a turn from a reductionistic approach in studying biological systems at increasing level, into a dis- covery process using high-throughput techniques (Figure 1.2). Ongoing research increases the wealth of contemporary biological information residing in some thousand public databases providing descriptive genomics, proteomics and enzyme information, gene expression, gene variants and gene ontologies. Refined explorative tools, such as new deep sequencing, along with the emergence of new specialized -omics (metabolomics, lipidomics, pharmacogenom- ics) and phenotyping techniques, constantly feed into this data pool and accelerate its growth. Given the enormous and heterogeneous amount of data, computational tools have become indispensable to mine, analyze, and connect such information. The aggregate of statistical 1 PROlOguE 3 FIGURE 1.2 By the evolution of scientific disciplines in biology over time, ever-smaller structures have come into focus and more detailed questions have been asked. With the availability of high-throughput sequencing techniques in genetics a turning point was reached at the molecular basis of life. The frontiers of research extended to hypothesis- free data acquisition of biological entities, with genomics becoming the first in a growing series of “-omics” disci- plines. Although functional genomics and proteomics are far from being completed, “omics” -type approaches addressing the phenotypical cellular, tissue and physiological levels constitute themselves as new scientific disci- plines, filling up an otherwise sparse data space. Computational systems biology provides methodologies to com- bine, model, and simulate entities on diverse (horizontal) levels of biological organization, such as gene regulatory and protein networks, and between these levels by using multiscale (vertical) approaches. bioinformatics tools to collect, store, retrieve, visualize, and analyze complex biological data has repeatedly proven useful in biological decision support and discovery. Deciphering the basic building blocks of life is a necessary step in biological research, but provides only lim- ited knowledge in terms of understanding and predictability. In the early stages the human genome project stirred the public expectation for a rapid increase in the deciphering of dis- ease mechanisms, more effective drug development and cure. However, it is well recognized that the battery of mechanisms involved in the proliferation of complex diseases like cancer, chronic diseases, or the development of dementias cannot be understood solely on the basis of knowing all its molecular components. As a consequence, a lack of system level understanding of cellular dynamics has prevented a substantial increase in the number of new drugs available for treatment, drug efficacy, or eradication of any specific diseases. In contrast, pharmaceutical companies are currently lack- ing criteria to select the most valuable targets, R&D expenses skyrocket, and new drugs rarely hit the market and often fail in clinical trials, while physicians face an increasing wealth of information that needs to be interpreted intelligently and holistically. Analysis of this dilemma reveals primary difficulties due to the enormous biomolecular complexity, structural and functional unknowns in a large portion of gene products and a lack of understanding of how the concert of molecular activities transfers into physiological alterations and disease. It has been long recognized that the understanding of cells as open systems, interacting with the environment, performing tasks and sustain homeostasis, or bet- ter homeodynamics (Yates 1992), requires the development of foundations for a general sys- tems theory that started with the seminal work of Bertalanffy (Von Bertalanffy 1969).
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