Introduction to Systems Biology Introduction to Systems Biology Edited by Sangdun Choi, PhD Department of Biological Sciences Ajou University Suwon, Korea © 2007 Humana Press Inc 999 Riverview Drive, Suite 208 Totowa, New Jersey 07512 www.humanapress.com For additional copies, pricing for bulk purchases, and/or information about other Humana titles, contact Humana at the above address or at any of the following numbers: Tel.: 973-256-1699; Fax: 973-256-8341, E-mail: order@humanapr.com; or visit our Website: http://www.humanapress.com All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise without written permission from the Publisher All articles, comments, opinions, conclusions, or recommendations are those of the author(s), and not necessarily reflect the views of the publisher This publication is printed on acid-free paper ANSI Z39.48-1984 (American National Standards Institute) Permanence of Paper for Printed Library Materials Photocopy Authorization Policy: Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by Humana Press Inc., provided that the base fee of US $30.00 per copy is paid directly to the Copyright Clearance Center at 222 Rosewood Drive, Danvers, MA 01923 For those organizations that have been granted a photocopy license for the CCC, a separate System of payment has been arranged and is acceptable to Humana Press Inc The fee code for Users of the Transactional Reporting Service is [978-1-58829-706-8 $30.00] 10 Library of Congress Control Number: 2006940362 ISBN: 978-1-58829-706-8 e-ISBN: 978-1-59745-531-2 Preface Introduction to Systems Biology is intended to be an introductory text for undergraduate and graduate students who are interested in comprehensive biological systems Because genomics, transcriptomics, proteomics, interactomics, metabolomics, phenomics, localizomics, and other omics analyses provide enormous amounts of biological data, systematic instruction on how to use computational methods to explain underlying biological meanings is required to understand the complex biological mechanisms and to build strategies for their application to biological problems The book begins with an introductory section on systems biology The experimental omics tools are briefly described in Part II Parts III and IV introduce the reader to challenging computational approaches that aid in understanding biological dynamic systems These last two parts provide ideas for theoretical and modeling optimization in systemic biological researches by presenting most algorithms as implementations, including the up-to-date, full range of bioinformatic programs, as well as illustrating available successful applications The authors also intend to provide a broad overview of the field using key examples and typical approaches to experimental design (both wetlab and computational) The format of this book makes it a great resource book and provides a glimpse of the state-of-the-art technologies in systems biology I hope that this book presents a clear and intuitive illustration of the topics on biological systemic approaches and further introduces ideal computational methods for the reader’s own research Sangdun Choi Department of Biological Sciences, Ajou University, Suwon, Korea v Contents Preface Contributors Part I Introduction Scientific Challenges in Systems Biology Hiroaki Kitano Bringing Genomes to Life: The Use of Genome-Scale In Silico Models Ines Thiele and Bernhard Ø Palsson From Gene Expression to Metabolic Fluxes Ana Paula Oliveira, Michael C Jewett, and Jens Nielsen Part II v xi 14 37 Experimental Techniques for Systems Biology Handling and Interpreting Gene Groups Nils Blüthgen, Szymon M Kielbasa, and Dieter Beule 69 The Dynamic Transcriptome of Mice Yuki Hasegawa and Yoshihide Hayashizaki 85 Dissecting Transcriptional Control Networks Vijayalakshmi H Nagaraj and Anirvan M Sengupta 106 Reconstruction and Structural Analysis of Metabolic and Regulatory Networks Hong-wu Ma, Marcio Rosa da Silva, Ji-Bin Sun, Bharani Kumar, and An-Ping Zeng 124 Cross-Species Comparison Using Expression Data Gaëlle Lelandais and Stéphane Le Crom 147 Methods for Protein–Protein Interaction Analysis Keiji Kito and Takashi Ito 160 vii viii Contents 10 11 Genome-Scale Assessment of Phenotypic Changes During Adaptive Evolution Stephen S Fong Location Proteomics Ting Zhao, Shann-Ching Chen, and Robert F Murphy Part III Modeling Spatiotemporal Dynamics of Multicellular Signaling Hao Zhu and Pawan K Dhar 14 Kinetics of Dimension-Restricted Conditions Noriko Hiroi and Akira Funahashi 15 Mechanisms Generating Ultrasensitivity, Bistability, and Oscillations in Signal Transduction Nils Blüthgen, Stefan Legewie, Hanspeter Herzel, and Boris Kholodenko 16 Employing Systems Biology to Quantify Receptor Tyrosine Kinase Signaling in Time and Space Boris N Kholodenko 17 Dynamic Instabilities Within Living Neutrophils Howard R Petty, Roberto Romero, Lars F Olsen, and Ursula Kummer 18 Efficiency, Robustness and Stochasticity of Gene Regulatory Networks in Systems Biology: l Switch as a Working Example Xiaomei Zhu, Lan Yin, Leroy Hood, David Galas, and Ping Ao 19 Applications, Representation, and Management of Signaling Pathway Information: Introduction to the SigPath Project Eliza Chan and Fabien Campagne Part IV 20 196 Theoretical and Modeling Techniques 12 Reconstructing Transcriptional Networks Using Gene Expression Profiling and Bayesian State-Space Models Matthew J Beal, Juan Li, Zoubin Ghahramani, and David L Wild 13 183 217 242 261 282 300 319 336 372 Methods and Software Platforms for Systems Biology SBML Models and MathSBML Bruce E Shapiro, Andrew Finney, Michael Hucka, Benjamin Bornstein, Akira Funahashi, Akiya Jouraku, Sarah M Keating, Nicolas Le Novère, Joanne Matthews, and Maria J Schilstra 395 Contents 21 22 23 24 25 CellDesigner: A Graphical Biological Network Editor and Workbench Interfacing Simulator Akira Funahashi, Mineo Morohashi, Yukiko Matsuoka, Akiya Jouraku, and Hiroaki Kitano DBRF-MEGN Method: An Algorithm for Inferring Gene Regulatory Networks from Large-Scale Gene Expression Profiles Koji Kyoda and Shuichi Onami Systematic Determination of Biological Network Topology: Nonintegral Connectivity Method (NICM) Kumar Selvarajoo and Masa Tsuchiya Storing, Searching, and Disseminating Experimental Proteomics Data Norman W Paton, Andrew R Jones, Chris Garwood, Kevin Garwood, and Stephen Oliver Representing and Analyzing Biochemical Networks Using BioMaze Yves Deville, Christian Lemer, and Shoshana Wodak 422 435 449 472 484 Appendices I Software, Databases, and Websites for Systems Biology 511 Glossary 517 Index 527 II ix Contributors Ping Ao Department of Mechanical Engineering, University of Washington, Seattle, WA, USA Matthew J Beal Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA Dieter Beule MicroDiscovery GmbH, Berlin, Germany Nils Blüthgen Institute of Theoretical Biology, Humboldt University, Berlin, Germany Benjamin Bornstein Machine Learning Systems Group, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Fabien Campagne Institute for Computational Biomedicine and Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, NY, USA Eliza Chan Institute for Computational Biomedicine and Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, NY, USA Shann-Ching Chen Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA Sangdun Choi Department of Biological Sciences, Ajou University, Suwon, Korea Marcio Rosa da Silva Research Group Systems Biology, GBF—German Research Centre for Biotechnology, Braunschweig, Germany xi xii Contributors Yves Deville Computing Science and Engineering Department, Université Catholique de Louvain, Louvain-la-Neuve, Belgium Pawan K Dhar RIKEN Genomic Sciences Centre, Yokohama, Kanagawa, Japan Andrew Finney Physiomics PLC Oxford, Oxford, UK Stephen S Fong Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA Akira Funahashi ERATO-SORST Kitano Symbiotic Systems Project, Japan Science and Technology Agency, Shibuya-ku, Tokyo, Japan David Galas Institute for Systems Biology, Seattle, WA, USA Chris Garwood School of Computer Science, University of Manchester, Manchester, UK Kevin Garwood School of Computer Science, University of Manchester, Manchester, UK Zoubin Ghahramani Department of Engineering, University of Cambridge, Cambridge, UK Yuki Hasegawa Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC), Yokohama Institute, Tsurumi-ku, Yokohama, Kanagawa, Japan Yoshihide Hayashizaki Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC), Yokohama Institute, Tsurumi-ku, Yokohama, Kanagawa, Japan Hanspeter Herzel Institute of Theoretical Biology, Humboldt University, Berlin, Germany Noriko Hiroi ERATO Kitano Symbiotic Systems Project, Japan Science and Technology Agency, Shibuya-ku, Tokyo, Japan Leroy Hood Institute for Systems Biology, Seattle, WA, USA Michael Hucka Division of Control and Dynamical Systems and Biological Network Modeling Center, California Institute of Technology, Pasadena, CA, USA 528 Index BioMAZE (cont.) Snow system, 501 VisualBioMAZE, 501 biochemical layers under, 489 biochemical network analysis under, 484–502 artificial intelligence and, 499–500 bipartite graphs for, 496 compound graphs for, 494–496 data models for, 486–487, 493–496 general graphs for, 496 reaction graphs for, 494–496 standard graph techniques for, 497–499 database management under, 501 Extended Entity Relationship under, 488, 501 gene expression profiles under, 485 graph management under, 501 implementation of, 500–502 PPIs and, 485 systemic layers under, 489 assembly/disassembly models and, 491–492 biochemical reactions and, 489 functional, 493 membrane transport and, 490–491 ontologies of, 493 protein expression and, 490 signal transduction and, 492–493 BioModels database, 411–412 BioPax (file format), 382 Bistability, 291–294 development of, 293 in phage λ model, 357–360 in ultrasensitive signaling cascades, 291–294 Boltzmann factor, 109 Bonferroni correction, 55 Boolean logic circuit models, 363–364 “Bow-tie structures,” 138–141 discovery of, 140 GSC in, 139–140 C CA See Cellular automata CAGE See Cap analysis gene expression Cancers, 7–8 Cap analysis gene expression (CAGE), 86, 92–93 SAGE v., 92–93 TSS and, 92 Catabolism, 38 Cdk See Cycline-dependant kinases CellDesigner, 422–433 applications of, 432 features of, 423–429 database connection capabilities, 428–429 exporting capabilities, 428 JRE and, 427 simulation capabilities, 428 supported environments for, 428 symbols as, 423–424, 426 worldwide group collaboration, 429 model creation under, 430 PANTHER pathway system and, 429 SBGN and, 422–424, 432–433 SBML and, 422–423, 426–429, 431–433 SBW and, 422–423, 427 CellML (file format), 381 Cells, 34–35 See also Subcellular location features; Subcellular locations genomes in, 34–35 metabolic engineering for, 40–41, 47–48 phenotypes in, 183 substrates for, 38 Cellular automata (CA), 242 CFD See Computational fluid dynamics CID See Collision-induced dissociation Clustering, 56 hierarchical, 56 K-means, 56 in location proteomics, 207–209 microarray technology and, 115–116 self-organized maps, 56 “similarity of genes” and, 56 Collagen homology (CH), 303 Collision-induced dissociation (CID), 162 Computational cellular dynamics, 10 Computational fluid dynamics (CFD), aerodynamic design and, engine combustion under, Navier-Stokes equation in, 9–10 Computer science, Constraint-based models, for metabolic network reconstruction, 19–23 assembly of, 22 biochemical reaction definitions within, 20–22 constraint identification within, 26–27 environmental, 26–27 physiochemical, 26 regulatory, 27 spatial, 26 evaluation of, 23 gap analysis in, 22 methods for, 27–28 Alternate Optima, 28 best/optimal, 27 OptKnock, 28–29 unbiased modeling, 29 ORFs and, 20 in silico, 19, 21 stoichiometry in, 20 substrate specificity within, 20 Control theory, modern, Cooperative binding, 290–291 Covalent modification cycles, 282–283 zero-order kinetics and, 284 Cross-species comparisons, 147–157 with DNA microarray, 148–149 expression data in, 148–150, 154–157 functional gene annotations and, 154–155 microarray standards for, 149–150 profile compendium of, 148–149 specific biological processes in, 149 gene-centered approaches to, 153–154 Index MiCoViTo, 153–154 yMGV, 153 gene pair definitions in, 150–152 functional gene annotation in, 151–152 homologs in, 151 orthologs in, 150–151, 155–157 paralogs in, 151 sequence conservation for, 150–151 gene sequences in, 147–148 global approaches to, 152–153 protein sequences in, 147–148 CSNDB (data model), 487 Currency metabolites, 131–132 “Curse of dimension,” 364 Cycline-dependant kinases (Cdk), 167–168 analysis of, 167–168 MS analysis of, 168 D DAG See Directed acrylic graphs DBD See DNA-binding domain DBRF-MEGN network See Difference-based regulation finding-minimum equivalent gene network Decoupling, 6–7 Deoxyribonucleic acid (DNA) binding sites for, 107, 109 DBD, 171–172 DNABook, 91–92 in ENCODE, 85 FL-cDNA, 85, 87–89, 90 captrapping of, 88–89 cloning of, 87 microarrays for, 92 in Mouse Encyclopedia Project, 86–90 selection problems with, 87 genomic sequencing for, 14 microarray technology for, 49 Depletion wave terms, 452–453 Diabetes, 327–328 neutrophils and, 327–328 during pregnancy, 329–330 Difference-based regulation finding-minimum equivalent gene (DBRF-MEGN) network, 435–446 algorithms for, 436–440 deduced edges deduction as, 436–437 edge grouping as, 439 essential edge selection as, 437 nonessential edge removal as, 437 uncovered edge selection as, 439 applications of, 440–443 large-scale gene profiles as, 440 MEGN validity and, 440–443 gene network inference, 435–436, 438 SDGs and, 436–437 software for, 443–445 applications of, 445 input file formats, 440–444 output file formats, 445 529 Dimension-restricted reaction kinetics (DRRK) modeling, 261–279 applications of, 263–264 from Basal theory, 264–266 fractal kinetics in, 264–266 Michaelis-Menten enzyme reactions in, 265–266 for biomolecular reactions, 266–269 pseudomonomolecular reactions and, 266–267 two-reactant, 267–269 calculation costs of, 276 diffusion coefficients in, 278–279 experiment planning for, 269–276 In vitro, 269–275 In vivo, 275–276 for FRAP data, 276 fundamental theory of, 263–264 “hopping” in, 263–264 “random percolation” in, 263 history of, 263 rate constants in, 278–279 steady state conditions in, 277–278 In Vivo reactions in, 261–263 Directed acrylic graphs (DAG), 74–75 Disease, Diabetes mellitus, fever and, 326 DNA See Deoxyribonucleic acid DNA-binding domain (DBD), 171–172 in PPIs, 171–172 DNABook, 91–92 DNA microarray technology, 49 DNA sequencing, for genomes, 14 DPInteract, 107 DRRK modeling See Dimension-restricted reaction kinetics modeling Dynamic Baysian networks (DBNs), 217–239 Kalman filter models, 217 LDS, 217, 220 microarray technologies for, 219 Occam’s Razor effects within, 225, 227 SSMs, 217–239 AUC in, 217, 234–235 EM for, 224 emphasis of, 219–220 Hinton diagrams for, 236, 238 input-dependent, 222 ML methods, 224 modeling time series with, 220–228 ODEs for, 230 realistic simulated data in, 229–232, 235 ROC analysis for, 217, 232–235 synthetic data in, 229, 237 T-cell activation of, 220 VBSSMs, 230–231 E EcoRv (E Coli enzyme), 269–273 differential equations for, 271 mass-action models for, 270–272 530 Index EFGR See Epidermal growth factor receptor EFM See Elementary flux mode EGF See Endothelial growth factor Elementary flux mode (EFM), 59 ENCODE See Encyclopedia of DNA Elements Encyclopedia of DNA Elements (ENCODE), 85 Endocytosis, in RTK signaling, 311–312 Endothelial growth factor (EGF), 249, 302–304 in RTK signaling, 302–304 CH linkers for, 303 computational modeling of, 302–303 “macrostates” in, 304 “macrovariables” for, 304 network complexity within, 303–304 scaffolds within, 304 EntrezGene, 70–72 gene group accession numbers for, 70–72 Enzyme Commission numbers, 23, 127 Enzyme Genomics Initiative, 127 EPD See Eukaryotic promoter database Epidemic states, Epidermal growth factor receptor (EFGR), 171 in PPIs, 171 in ultrasensitive signaling cascades, 288 Escherichia coli (E coli), 377 in signaling pathways, 377 Eukaryotes, 107 oscillations in, 320 signaling networks in, 282 TRN reconstruction methods for, 130 Eukaryotic promoter database (EPD), 79 Event action tables, signaling network integration with, 246–247 Evolvability, 5, 7–8 signaling networks and, 242–243 Expression data, 148–150, 154–157 functional gene annotations and, 154–155 microarray standards for, 149–150 profile compendium of, 148–149 specific biological processes in, 149 Extended Entity Relationship, 488, 501 EXtensible Markup Language (XML), 395–396 SBML and, 395–396 schemas for, 411 CellML, 381 evolution of, 381 HUPO PSI, 381 SBML, 381 for SigPath Project, 384 Filtering tasks, 223 FL-cDNA See Full-length cDNA Fluorescence recovery after photobleaching (FRAP) analysis, 275 DRRK modeling and, 276 Flux balance analysis (FBA), 42, 48–49 of Omics data, 48–49 in predictive models for metabolic engineering, 58–59 Fluxomics, 193 FPs See False positives Fractal kinetics, 264–266 FRAP See Fluorescence recovery after photobleaching analysis Fructose 1,6-biphosphate (FBP), 449, 464–465 Full-length cDNA (FL-cDNA), 85, 87–89, 90 captrapping of, 88–89 cloning of, 87 microarrays for, 92 in Mouse Encyclopedia Project, 86–90 selection problems with, 87 Functional Annotation of the Mouse (FANTOM), 78, 85–86, 94–100 FANTOM1, 94–96 FANTOM2, 94–96 FANTOM3, 96–100 CAGE data in, 99 dataset resources, 96 functional RNA research for, 98–99 gene definitions within, 97 ncRNA in, 98–99 novel NRN continent of, 96–97 S/AS RNA in, 99 TD in, 96 TF in, 96 TK in, 96 TU decrease in, 97, 99 RTPS pipeline for, 95 FWER See Family-wise error rates F False positives (FPs), 173 biological, 173 in PPIs, 173 technical, 173 Family-wise error rates (FWER), 76 FANTOM See Functional Annotation of the Mouse FBA See flux balance analysis FBP See Fructose 1,6-biphosphate Fevers, 326–327 disease and, 326 Fields, Stanley, 171 File formats, 381 in signaling pathways, 381–382 BioPax, 382 G GA See Genetic algorithm Galactose utilization pathways, 44 with Omics data, 46 GAL systems, 46–47 See also Galactose utilization pathways Gap analysis, 22 gap filling and, 22 in metabolic network reconstruction, 22 Gap filling, 22 Gas chromatography-mass spectrometry (GC-MS), 52 Gaussian white noise, 357–359 GC-MS See Gas chromatography-mass spectrometry GEF See Guanine nucleotide exchange factor Index GenBank, 70, 72–73 gene group accession numbers of, 70, 72–73 Gene groups, 69–81 See also Mouse Encyclopedia Project accession numbers for, 70–71 databases v., 72 EntrezGene and, 70–72 EST in, 70 GenBank and, 70, 72–73 HomGL and, 71–73 homologs for, 71 LocusLink, 71, 73 NCBI and, 70, 73 RefSeq and, 71, 73 SwissProt and, 70 UniGene and, 70–71, 73–74 analysis pipeline for, 80 conversion of, 72 DAG for, 74–75 EPD, 79 FANTOM, 78, 85–86, 94–100 frequency of, 75 functional interpretation of, 74–78 multiple testing, 76–77 GO annotations for, 69, 77–78 data sources for, 74 FDR with, 76 FWER with, 76 profiling with, 74–76 software for, 77–78 Mouse Genome Database, 74 phylogenetic footprinting for, 79 TSSs for, 78, 86 Gene identification signature (GIS), 86, 93–94 Gene ontology (GO), 69, 74–76 data sources for, 74 FDR with, 76 FWER with, 76 profiling for, 74–76 proteomics and, 197 signaling pathways and, 376–377 software for, 77–78 Gene Ontology (GO) Consortium, 376 Genes, cross-species comparisons and, 147–148 in genomes, 38 groups, 69–81 accession numbers for, 70–71 inference networks, 435–436, 438 regulatory networks for, Gene signature cloning (GSC), 86 in “bow-tie structures,” 139–140 Genetic algorithm (GA), 467 Genetic engineering, 40 Genome reconstruction, 16–17 bilinear transformation in, 17 chemical reaction rates as factor for, 17–18 reaction stoichiometry in, 17 steady-state networks and, 18 thermodynamics as factor in, 17 Genomes, 14–34 BIGG structured databases for, 14–15 graphical representations of, 15 mathematical representations of, 15 textual representations of, 15 in cells, 34–35 DNA sequencing for, 14 genes within, 38 metabolic network reconstruction and, 15, 18–23 1D annotation for, 18–20, 22 automation of, 23 constraint-based model formulation for, 19–23 information sources for, 18–19 organism properties for, 15 systems boundaries within, 25–26 in mice transcriptome analysis, 101–102 reconstruction of, 16 transcriptional analysis for, 49 Genome-scale metabolic networks, 41–42 FBA in, 42 ORFs and, 41 reporter metabolites as part of, 42 Genome sequencing, 38–40 for DNA, 14 in metabolic networks, 124, 126 in TRNs, 124, 126 Genomics, in adaptive evolution, 186–187 genotype-phenotype analogies for, 187 infrastructure analogies for, 187 Genotypes, 186–187 in adaptive evolution, 186–187 Giant strong components (GSC), 139–140 GIS See Gene identification signature Glycolysis, 323–325 in neutrophils, 323–325 GO See Gene ontology Goldbeter-Koshland switch, 287 GPCRs See G protein-coupled receptors G protein-coupled receptors (GPCRs), 300–301 GTP exchange for, 301 Graph theory biochemical networks and, 494–496 bipartite graphs in, 496 compound graphs in, 494–496 general graphs in, 496 reaction graphs in, 494–496 standard graph techniques for, 497–499 BioMAZE and, 501–502 database management under, 501–502 for metabolic networks, 125, 131–134 metabolite graphs as part of, 131–132 reaction graphs as part of, 131 for TRNs, 131–134 Green fluorescence protein (GFP), 198 for subcellular location, 198 GSC See Gene signature cloning; Giant strong components Guanine nucleotide exchange factor (GEF), 305 531 532 Index H Hexose monophosphate shunt (HMS) activation, 319 NAD[P]H and, 330 during pregnancy, 328–329 Highly Optimized Tolerance models, High-performance liquid chromatography (HPLC), for whole cell measurements, 190–191 Hill coefficients, 283–286, 290 in ultrasensitive signaling cascades, 283–284 Hinton diagrams, 236, 238 HMS activation See Hexose monophosphate shunt activation HomGL, 71–73 for gene group accession numbers, 71–72 Homologs in cross-species comparisons, 151 for gene group accession numbers, 71 orthologs as, 151 paralogs as, 151 “Hopping,” 263–264 “Horizontal basic science,” 102 HPLC See High-performance liquid chromatography HPRD See Human Protein Reference Database Human Genome Project, 337 Human Protein Reference Database (HPRD), 380 Human Proteome Organization, 474 HUPO PSI (file format), 381 I ICAT See Isotope/coded affinity tag strategy Information theoretic weight matrix, 108–114 SVM as part of, 110–114 conventional, 111 Gaussian probabilities in, 110–111 one-class, 111 QPMEME and, 111 ROC analysis of, 111–112 SELEX methods, 110 INOH (data model), 487 “In Silico Design and Adaptive Evolution of Escherichia coli for Production of Lactic Acid,” 31–33 OptKnock in, 31–32 In silico models, for metabolic networks, 19, 21, 29–33 “In Silico Design and Adaptive Evolution of Escherichia coli for Production of Lactic Acid,” 31–33 “Integrating High-throughput and Computational Data Elucidates Bacterial Networks,” 29–31 “Integrating High-throughput and Computational Data Elucidates Bacterial Networks,” 29–31 Integrative models, for metabolic engineering, 56–58 NCA as part of, 58 Interaction sequence tag (IST), 174 in PPIs, 174 Interactome mapping, 173–175, 179 for PPIs, 173–175, 179 International Union of Biochemistry and Molecular Biology (IUBMB), 127 In vitro reactions, 262, 269–275 in DRRK models, 269–275 EcoRv and, 269–273 experiment planning for, 269–275 reaction order estimations for, 272–274 simulation results of, 274–275 In vivo v., 262 in phage λ model, 347–348 In vivo reactions in DRRK modeling, 261–263, 275–276 experiment planning for, 275–276 fractal kinetics and, 275 FRAP analysis for, 275 ODEs and, 262 PDEs and, 262 In vitro v., 262 in phage λ model, 347–348, 351–353 Kramers rate formula in, 352 Isobaric tags for relative and abundance qualifications (iTRAQ) methods, 52 Isotope/coded affinity tag (ICAT) strategy, 52 in stable isotope labeling, 169–170 IST See Interaction sequence tag iTRAQ methods See Isobaric tags for relative and abundance qualifications methods IUBMB See International Union of Biochemistry and Molecular Biology J Java Runtime Environment (JRE), 427 JRE See Java Runtime Environment K Kalman filter models, 217 Kinetics fractal, 264–266 zero-order, 284 Kite networks, 136–137 measurements for, 137 K-means clustering, 56 Kramers rate formula, 351–352 in phage λ model, 352 L Large-scale analysis, for proteins, 165–167 data set comparisons in, 166–167 FLAG in, 165–167 genome-wide, 167 ORFs and, 167 TAP in, 165–167 LC See Liquid chromatography LC-MS See Liquid chromatography-mass spectrometry LibSBML (Systems Biology Mark-Up Language library), 409 API and, 409 Linear dynamical systems (LDS), 217 Lipopolysaccharide (LPS), 325 NAD[P]H and, 325 Index Liquid chromatography (LC), 162 HPLC, 190–191 LC-MS, 52 protein identification with, 163 Liquid chromatography-mass spectrometry (LC-MS), 52 protein identification with, 163 LocusLink, 71, 73 gene group accession numbers for, 71, 73 LPS See Lipopolysaccharide M MALDI See Matrix-assisted laser desorption/ ionization MAPK See Mitogen-activated protein kinase cascade Mass spectrometry (MS), 51 of Cdk, 168 under PEDRo model, 477 for PPIs, 160–171 tandem, 161 CID in, 162 LC for, 162 TOF, 161 Mass spectrometry, tandem (MS/MS), 160–162 Mass spectrometry, time-of-flight (TOF-MS), 161 MathSBML, 395, 400–401, 410, 412–420 API command control under, 413 command summary, 414 mathematical expressions in, 400–401 model editors under, 418–420 model imports for, 414–415 names under, 415–416 simulation models for, 416–418 subsets of, 401 summary of, 413 variable scoping for, 415–416 MATLAB, 410 Matrix-assisted laser desorption/ionization (MALDI), 161 Maturation-promoting factor (MPF), 424–425 process diagram for, 425 in SBGN, 424–425 MCA See Metabolic control analysis Melatonin, 323–324, 326 NAD[P]H and, 324 neutrophils and, 323–324 Messenger RNA (mRNA), 191–192 genome-scale measurements for, 191–192 Metabolic control analysis (MCA), 285–286 Metabolic engineering, 40–41 for cells, 40–41, 47–48 models for, 53–59 classical, 54–56 integrative, 56–58 with Omics data, 45–49, 53 2DE and, 51 clustering for, 56 FBA and, 48–49 galactose utilization pathways and, 46 GC-MS and, 52 ICAT strategy for, 52 iTRAQ method and, 52 LC-MS and, 52 metabolite profiling and, 46 MS and, 51 PCA for, 46, 55–56 predictive models, 53, 58–59 quantification of, 49–53 signaling network reconstruction and, 46–47 statistical significance analysis of, 54–55 technology summary for, 50–51 traits identification and, 45–46 Penicillium chrysogenum and, 40 prediction of, 48–49 reverse, 45 transcriptome analysis in, 49–53 Metabolic fluxes, 38, 41–45 GAL system and, 44 metabolic networks and, 41–45 quantitative analysis of, 52–53 regulation of, 42–45 “Metabolic footprinting,” 191 Metabolic networks, 38, 41–45, 124–143 See also Metabolic networks, reconstruction of enzyme databases for, 126 genome-scale, 41–42 FBA in, 42 ORFs and, 41 reporter metabolites as part of, 42 genomic sequencing in, 124, 126 graph theory for, 125, 131–134 integration of, 130–131, 134 IUBMB and, 127 metabolic fluxes and, 41–45 GAL system and, 44 regulation of, 42–45 metabolites in, 138 for Streptococcus pneumonia, 132 structural analysis of, 134–143 APL in, 134–136 “bow-tie,” 138–141 degree distribution as part of, 134–136 multilayer acyclic structures and, 141–143 network centrality as part of, 136 scale-free networks and, 134 Metabolic networks, reconstruction of, 15, 18–34 1D annotation for, 18–20, 22 2D annotation for, 34 3D annotation for, 33 4D annotation for, 33 automation of, 23 Enzyme Commission numbers for, 23 Pathway Tools for, 23 constraint-based model formulation for, 19–23 assembly of, 22 biochemical reaction definitions within, 20–22 constraint identification within, 26–27 evaluation of, 23 533 534 Index Metabolic networks, reconstruction of (cont.) gap analysis in, 22 ORFs and, 20 in silico, 19, 21 stoichiometry in, 20, 24–25 substrate specificity within, 20 Enzyme Commission numbers in, 127 Enzyme Genomics Initiative and, 127 genome-based, 125–126 high-quality, 127–128 information sources for, 18–19 growth performance as, 19 medium composition as, 19 secretion products as, 19 matrix representations of, 24–25 network states analysis tools for, 27–29 Alternate Optima as, 28 best/optimal, 27 OptKnock as, 28–29 unbiased modeling as, 29 ORFs in, 125 organism properties for, 15 pathways for, 128 In silico models for, 19, 21, 29–33 “In Silico Design and Adaptive Evolution of Escherichia coli for Production of Lactic Acid,” 31–33 “Integrating High-throughput and Computational Data Elucidates Bacterial Networks,” 29–31 systems boundaries within, 25–26 TRN reconstruction v., 129 Metabolism, 38 See also Metabolic networks; Metabolic networks, reconstruction of oscillatory, 319 processes of, 38 systems biology and, 39 Metabolites currency, 131–132 in graph theory, 131–132 “metabolic footprinting” and, 191 in metabolic networks, 138 in metabolomics, 193–194 Omics data profiling of, 46 reporter, 42, 48 Streptococcus pneumonia and, 132 whole cell measurements and, 190–191 by HPLC, 190 Metabolomics, 193–194 metabolites in, 193–194 Michaelis-Menten enzyme reactions, 265–266 MiCoViTo See Microarray Comparison Visualization Tool Microarray Comparison Visualization Tool (MiCoViTo), 153–154 Microarray Gene Expression Data Society, 149 Microarray technology clustering and, 115–116 in cross-species comparisons, 148–149, 149–150 for DBNs, 219 for DNA, 49 for FL-cDNA, 92 MiCoViTo, 153–154 yMGV, 153 Minimization of metabolic adjustment (MOMA), 59 Mitogen-activated protein kinase cascade (MAPK), 282 endocytosis in, 311–312 RTK signaling and, 302, 307, 310–311 ultrasensitive signaling cascades and, 284, 292 Modern control theory See Control theory, modern Molecular biology, MOMA See Minimization of metabolic adjustment Motifs, in transcriptional control networks, 116–117, 119 Mouse Encyclopedia Project, 86–94 DNABook and, 91–92 FL-cDNA use in, 86–90 cloning of, 87 microarrays of, 92 high throughput sequence analysis systems in, 91 internal cleavage avoidance in, 88 mouse choice in, 86 mRNA elongation strategies for, 88 new vector constructions for, 90 normalization/subtraction technologies in, 90–91 RISA in, 91 transcriptome dataset for, 86 CAGE data in, 86, 92–93 GIS data in, 86, 93–94 GSC data in, 86, 93–94 Mouse Genome Database, 74 MPO See Myeloperoxidase mRNA See Messenger RNA mRNA elongation strategies, 88 in Mouse Encyclopedia Project, 88 RT for, 88 MS See Mass Spectrometry MS/MS See Mass spectrometry, tandem Multilayer acyclic structures, 141–143 for metabolic networks, 141–143 for TRNs, 141–143 Myeloperoxidase (MPO), 319, 322–323 cycle for, 323 experimental verification of, 323 in neutrophils, 322–323 N NAD[P]H See Nicotinamide adenine dinucleotide National Center for Biotechnology Information(NCBI), 70, 73, 126 gene group accession numbers of, 70, 73 National Institutes of Health (NIH), 101 Navier-Stokes equation, 9–10 NCA See Network component analysis ncRNA See Noncoding RNA Nerve growth factor (NGF), 302 Network centrality, 136 “closeness,” 136 Kite networks and, 136 measurements for, 137 Index Network component analysis (NCA), 58 Neutrophils, 319–333 activation of, 332 Belousov-Zhabotinskii reaction and, 320 biomechanisms of, 325–331 diabetes and, 327–328 endogenous factors and, 326 exogenous factors and, 325–326 fevers, 326–327 LPS in, 325 PMA and, 326 pregnancy immunomodulation and, 328–331, 333 computation biology of, 321–325 glycolysis in, 323–325 MPO in, 322–323 NAD[P]H in, 322 HMS activation in, 319 Melatonin and, 323–324 as model system, 320–321 MPO translocation in, 319 oscillations and, 319–321 NGF See Nerve growth factor NICD See Notch intracellular domain Nicotinamide adenine dinucleotide (phosphate) (NAD[P]H), 320, 322–325, 330–332 HMS enzymes and, 330 LPS and, 325 Melatonin concentrations and, 324 neutrophils and, 322 NIH See National Institutes of Health Noncoding RNA (ncRNA), 85 in FANTOM 3, 98–99 Nonintegral Connectivity Method (NICM), 449–465 applications of, 452–455 computational implementations under, 468 connectivity rules for, 455–462 feedback motifs under, 462 feedforward motifs under, 462 linear-chain motifs under, 455–457, 459–461 FBP under, 449, 464–465 fitness values for, 467–468 GA under, 467, 470 local network connectivity flowchart for, 453 methods for, 450–462 depletion wave terms and, 452–453 perturbation coefficients and, 451–452, 457–459 steady-state interaction maps in, 450 tolerance under, 468 yeast glycolic network analysis under, 463–464, 468–469 S cerevisiae, 463–464, 468 Nonlinear dynamics theory, “Nonself” biological entities, 6–7 Notch intracellular domain (NICD), 250 Notch signaling propagation models, 249–256 captured signaling in, 251 EGF in, 249 gene expression profiles for, 252 methods of, 250–251 NICD in, 250 ODE for, 250 PSM in, 249–250 results of, 251–252 signaling profiles for, 252 O Object-oriented programming (OOP), 242 Occam’s Razor, 225, 227, 338 effects of, 225, 227 OD See Optical density ODEs See Ordinary differential equations Omics data, 45–53 2DE and, 51 clustering for, 56 hierarchical, 56 K-means, 56 self-organized maps, 56 “similarity of genes” and, 56 FBA of, 48–49 galactose utilization pathways with, 46 GC-MS and, 52 ICAT strategy for, 52 iTRAQ method and, 52 LC-MS and, 52 metabolite profiling and, 46 MS and, 51 PCA for, 46, 55–56 PC1, 55 PC2, 55 predictive models with, 53, 58–59 biomass production within, 59 EFM in, 59 FBA in, 58–59 MOMA in, 59 reporter metabolites in, 58 quantification of, 49–53 signaling network reconstruction with, 46–47 GAL system and, 46–47 statistical significance analysis of, 54–55 Benjamin-Hochberg correction, 55 Bonferroni correction, 55 technology summary for, 50–51 traits identification with, 45–46 OOP See Object-oriented programming Open reading frames (ORFs), 20 genome-scale metabolic networks and, 41 in large-scale protein analysis, 167 in metabolic network reconstruction, 125 subcellular location prediction and, 199 in Y2H, 173 Optical density (OD), 189 OptKnock, 28–29 in “In Silico Design and Adaptive Evolution of Escherichia coli for Production of Lactic Acid,” 31–32 Ordinary differential equations (ODEs), 230 in Notch signaling propagation models, 250 for SBML, 422 In vivo reactions and, 262 535 536 Index ORFs See Open reading frames Orthologs, 150–151, 155–157 Oscillations, 296, 319–321 chemical, 320 in eukaryotes, 320 NAD[P]H in, 320 neutrophils and, 319–321 in prokaryotes, 320 RTK signaling and, 302 P PANTHER pathway system, 429 Paralogs, 151 Partial differential equations (PDEs), 253 in PCP models, 253 In vivo reactions and, 262 Pathway Tools, 23 PCA See Principal component analysis PCP models See Planar cell polarity models PDEs See Partial differential equations PEDRo model See Proteomics Experimental Data Repository model Penicillium chrysogenum, 40 Peptide mass fingerprinting (PMF), 160–161 MALDI for, 161 trypsin in, 161 Phage λ model, 336–365 controlling regions of, 342 diagrams of, 342 genetic switch in, 337–348 bistability of, 357–360 Gaussian white noise in, 357–359 life cycle of, 339–340 modeling strategies for, 340–341 robustness in, 338, 356–357, 361 spontaneous induction in, 339 mathematical modeling for, 361–364 predictions of, 361–362 modeling methodologies in, 359, 362–364 Boolean logic circuit, 363–364 “curse of dimension” in, 364 empirical, 363 literature sampling, 363 principle, 363 quantitative modeling for, 342–348 binding configurations in, 341–343 deterministic, 343–347 homeostatic equilibrium in, 343 operator configurations in, 344 parameters in, 345 In vivo v In vitro with, 347–348 stochastic dynamical structure of, 336, 342, 348–351, 353–355 analysis of, 350–351 driving force potential gradients in, 351 friction, 350–351 Kramers rate formula in, 351 minimum quantitative model and, 348–350 transverse force in, 351 theory v experiment for, 351–361 epigenetic state lifetime in, 360 protein distribution in, 360 relaxation time in, 360 In vivo parameters and, 351–353 wild type, 353–354 lytic switching in, 358 Phenotype plasticity, Phenotypes genome-scale measurements for, 191–194 fluxomics and, 193 metabolomics and, 193–194 mRNA and, 191–192 proteins and, 192 proteomics and, 192 signaling pathways and, 373–374 whole cell measurements for, 188–191 growth rates and, 188–190 metabolite secretions and, 190–191 OD and, 189 respiration rates and, 190 robustness and, 189 Phenotypes, whole cell, 183, 186–187 in adaptive evolution, 186–194 measurements for, 188–191 growth rates and, 188–190 metabolite secretions and, 190–191 OD and, 189 respiration rates and, 190 robustness and, 189 in PCP models, 255 Phorbomyristate acetate (PMA), 326 Phylogenetic footprinting, 79 transcriptional control networks and, 116–117, 119 Pierre (PEDRo application), 480–482 Planar cell polarity (PCP) models, 252–256 features of, 253–255 intracellular movement during, 254 methods for, 253–255 PDE in, 253 phenotypes in, 255 results for, 255–256 in signaling networks, 252–256 PMA See Phorbomyristate acetate PPIs See Protein-Protein Interactions Prediction tasks, 223–224 Predictive models, for metabolic engineering, 53, 58–59 biomass production within, 59 EFM in, 59 FBA in, 58–59 MOMA in, 59 reporter metabolites in, 58 Pregnancy, 328–331 diabetes during, 329–330 HMS enzymes during, 328–329 immunomodulation during, 328–331, 333 immunoregulation during, 330–331 physiologic regulations during, 328–330 trophoblasts during, 330–331 Presomitic mesoderm (PSM), 249–250 Index Principal component analysis (PCA), 46, 55–56 Prokaryotes, 107 oscillations in, 320 TRN reconstruction methods for, 129 Protein Atlas project, 200 Protein-Protein Interactions (PPIs), 160–179 BioMAZE and, 485 MS for, 160–171 affinity purification and, 164–165 in complex samples, 162–163 EFGR and, 171 focused analysis in, 167–169 large-scale analysis in, 165–167 MS/MS with, 160–162 PMF and, 160–161 protein identification with, 160–163 stable isotope labeling for, 169–171 TNF and, 169 Y2H-based, 160, 171–179 AD in, 172 alternative, 178–179 benefits/disadvantages of, 172–173 DBD in, 171–172 FPs in, 173 interactome mapping for, 173–175, 179 IST in, 174 principles of, 171–172 reverse, 175–177 split ubiquitin system in, 178–179 three-hybrid systems and, 177–178 Proteins, 4, 38 See also Protein-Protein Interactions; Proteomics actin, 38 architecture of, 168 bait, 164 Cdk, 167–168 cross-species comparisons and, 147–148 genome-scale measurements for, 192 large-scale analysis for, 165–167 phosphoproteins, 310–311 Protein Atlas project and, 200 in proteomics, 192 in regulatory networks, 38 ubiquitin, 178–179 Proteomics, 192, 196–212, 472–482 databases for, 474–475 experimental processes under, 473 GO annotations and, 197 Human Proteome Organization and, 474 location, 196–212 clustering in, 207–209 focus of, 196–197 knowledge-capture approach to, 197 sequence prediction from location approach to, 197–198 PEDRo model, 475–482 API and, 481 data capturing under, 478–482 future applications of, 482 MS under, 477 537 Pierre as part of, 480–482 protein separation under, 477 Protein Atlas project and, 200 PSI, 474 subcellular locations and, 198, 200–212 automated analysis for, 200–207 GFP for, 198 image databases and, 199–200 image segmentation of, 202–203 immunofluorescence for, 198 ORFs and, 199 pattern models for, 209–212 protein-tagging methods for, 198–199 SLFs for, 200–201 trees for, 209 Proteomics Experimental Data Repository (PEDRo) model, 475–482 API and, 481 data capturing under, 478–482 future applications of, 482 MS under, 477 Pierre as part of, 480–482 protein separation under, 477 Proteomics Standard Initiative (PSI), 474 PSI See Proteomics Standard Initiative PSM See Presomitic mesoderm Q QPMEME See Quadratic programming method for energy matrix estimation Quadratic programming method for energy matrix estimation (QPMEME), 111–114 for dinucleotide models, 112–114 extended, 112–114 R “Random percolation,” 263 Reaction graphs, 131 Reaction stoichiometry in genomic reconstruction, 17 in metabolic network reconstruction, 20 Receiver operating characteristic (ROC), 217, 232–235 sensitivity as, 232 specificity as, 232 for SSMs, 217, 232–235 Receptor tyrosine kinase (RTK) signaling, 300–313 autophosphorylation of, 301 complex temporal dynamics for, 305–306 feedback loops and, 306 GEF and, 305 EGFR network in, 302–304 CH linkers for, 303 computational modeling of, 302–303 “macrostates” in, 304 “macrovariables” for, 304 network complexity within, 303–304 scaffolds within, 304 endocytosis in, 311–312 GPCRs in, 300–301 malfunctions of, 301 538 Index Receptor tyrosine kinase (RTK) signaling (cont.) MAPK and, 302, 307, 310–311 NGF in, 302 oscillations and, 302 phosphoprotein gradients during, 310–311 scaffolding, 312–313 spatial dimensions of, 306–311 gradients in, 307–310 membrane recruitment in, 307 scaffolds in, 307 universal cycle motifs in, 305 Reconstruction See Metabolic networks, reconstruction of RefSeq, 71, 73 gene group accession numbers for, 71, 73 RegulonDB, 107 Reporter metabolites, 42, 48 in predictive models, 58 Representative Transcript and Protein Sets (RTPS), 95 Reverse metabolic engineering, 45 Reverse transcriptase (RT), 88 Reverse Y2H See Reverse yeast two-hybrids Reverse yeast two-hybrids (Reverse Y2H), 175–177 dual-bait, 176 interaction-defective allele isolation with, 175 mapping interaction domains through, 175 separate-of-function alleles isolation with, 175–176 Riken Integrated Sequencing Analysis (RISA), 91 RISA See Riken Integrated Sequencing Analysis (RISA) Robustness, 5–8 cancers and, 7–8 decoupling and, 6–7 Diabetes mellitus and, disease and, diversity as part of, of epidemic states, evolvability and, 5, 7–8 fail/safe mechanisms for, feedback loop control and, Highly Optimized Tolerance models and, modularity and, 6–7 “nonself” biological entities and, 6–7 of phage λ model, 338, 356–357, 361 phenotype plasticity and, in phenotypes, 189 redundancy and, in systems biology, 5–8 tradeoffs between, ROC See Receiver operating characteristic RT See Reverse transcriptase RTK signaling See Receptor tyrosine kinase signaling RTPS See Representative Transcript and Protein Sets S Saccharomyces, 117–119 phylogenetic tree for, 117 site conservation among, 118 yeast glycolic network analysis of, 463–464 for S cerevisiae, 463–464 SAGE See Serial analysis of gene expression SBGN See Systems Biology Graphical Notation SBML See Systems Biology Mark-Up Language SBW See Systems Biology Workbench Scaffolds, 304 in RTK signaling, 304, 307, 312–313 in EGFR network, 304 spatial dimensions for, 307 Scale-free networks, 134 SDA See Stepwise discriminate analysis SDGs See Signed direct graphs SELEX methods, 107 for SVM, 110 Sense/anti-sense (S/AS) pairing, 85 in FANTOM 3, 99 Serial analysis of gene expression (SAGE), 92 CAGE v., 93 TSS within, 94 Signaling networks, 242–258 See also Receptor tyrosine kinase signaling; Ultrasensitive signaling cascades adjacency matrix as, 248–249 CA in, 242 connectors within, 243–244 discrete molecular, 245–246 dynamic capture for, 247–248 in eukaryotes, 282 event action tables integration with, 246–247 event-driven computation in, 245 evolvability and, 242–243 molecular interactions in, 246 Notch signaling propagation models, 249–256 EGF in, 249 gene expression profiles for, 252 methods of, 250–251 NICD in, 250 ODE for, 250 PSM in, 249–250 results of, 251–252 signaling profiles for, 252 OOP in, 242 parallel, 244–245 PCP models, 252–256 features of, 253–255 intracellular movement during, 254 methods for, 253–255 PDE in, 253 results for, 255–256 reconstruction of, 248–249 regulators within, 244 state transition map for, 243 topology of, 257 two-tier parallelism for, 244–245 Signaling pathways, 372–383 applications for, 375–378 biochemical modeling in, 378 fact searches in, 375–376 Index gene context in, 376 with gene ontology, 376–377 interaction database in, 377 network properties in, 378 statistical analyses in, 377 structural properties in, 377–378 in biomedical research, 379–383 cartoon representations in, 380 databases for, 382–383 file formats in, 381 HPRD and, 380 ontologies in, 382 structured representations in, 380–383 text representations in, 379–380 E coli and, 377 exogenous chemicals and, 372 modeling for, 374–375 phenotypes and, 373–374 in SigPath Project, 383–389 architecture of, 383–384 data collection for, 384–385 data deletion within, 388–389 data transfers within, 387–388 file format for, 384 information management approach to, 384 literature level for, 385–386 ontology of, 384 qualitative level for, 386 quantitative level for, 386–387 Signal transduction under BioMAZE, 492–493 in systems biology, 4–5 Signed direct graphs (SDGs), 436–437 SigPath Project, 383–389 architecture of, 383–384 data collection for, 384–385 data deletion within, 388–389 data transfers within, 387–388 file format for, 384 information management approach to, 384 literature level for, 385–386 ontology of, 384 qualitative level for, 386 quantitative level for, 386–387 “Similarity of genes,” 56 SLFs See Subcellular location features Smoothing tasks, 223 Snow system, 501 SOFs See Subcellular object features Spontaneous induction, 339 SSMs See State-space models, Linear-Gaussian Stable isotope labeling, 169–171 methods of, 169–171 chemical, 169–170 ICAT, 169–170 metabolic, 170–171 PPIs and, 169–171 MS for, 169–171 trypsin in, 170 State-space models (SSMs), Linear-Gaussian, 217–239 AUC in, 217, 234–235 EM for, 224 Hinton diagrams for, 236, 238 input-dependent, 222 ML methods, 224 modeling time series with, 220–228 ARD and, 225, 227 data feedback in, 221–222 dimensionality determinations in, 226–228 gene expression and, 222–223 hidden state correlations in, 236, 238 hyperparameters of, 225 Occam’s Razor effect and, 225 output success of, 221 parameter learning in, 234 prior specifications in, 224–226 state estimation in, 223–224 topology of, 220–222 variables of, 220–222 ODEs for, 230 realistic simulated data in, 229–232, 235 ROC analysis for, 217, 232–235 synthetic data in, 229, 237 VBSSMs, 230–231 Statistical significance analysis, 54–55 Benjamin-Hochberg correction, 55 Bonferroni correction, 55 Steady-state networks, 18 Stepwise discriminate analysis (SDA), 203 Stochasticity, 348–349 in systems biology, 348–349 Stoichiometric inhibition, 290 Stoichiometry See Reaction stoichiometry Streptococcus pneumonia metabolic networks for, 132 metabolite graphs for, 132 Subcellular location features (SLFs), 200–206 2-D, 201, 203–206 3-D, 202–206 alternative image classification of, 206 classification of, 203–206 Subcellular locations, 198, 200–212 automated analysis for, 200–207 feature selection for, 203 SDA and, 203 GFP for, 198 image databases and, 199–200 immunofluorescence for, 198 ORFs and, 199 pattern models for, 209–212 generative, 211–212 object-based, 210–211 SOFs in, 210 protein-tagging methods for, 198–199 in proteomics, 198, 200–212 SLFs for, 200–206 539 540 Index Subcellular locations (cont.) 2-D, 201, 203–206 3-D, 202–206 alternative image classification of, 206–207 classification of, 203–206 trees for, 209 Subcellular object features (SOFs), 210 Substrates for cells, 38 precursor metabolites and, 38 Support vector machines (SVM), 110–114 conventional, 111 Gaussian probabilities in, 110–111 one-class, 111 QPMEME and, 111–114 for dinucleotide models, 112–114 extended, 112–114 ROC analysis of, 111–112 SELEX methods, 110 SVM See Support vector machines SwissProt, 70, 73 gene group accession numbers for, 70, 73 Systems biology, 3–11, 17–18, 39 adaptive evolution and, 184–186 methodology for, 186 cellular regulatory circuits in, 106 central dogma of, 43 control methods within, design for, dynamics within, gene regulatory networks in, metabolism and, 39 properties of, 17–18, 422 robustness in, 5–8 decoupling and, 6–7 disease and, diversity as part of, of epidemic states, evolvability and, 5, 7–8 fail/safe mechanisms for, feedback loop control and, Highly Optimized Tolerance models and, modularity and, 6–7 “nonself” biological entities and, 6–7 self-organization in, 319–320 signal transduction in, 4–5 steady-state networks and, 18 stochasticity in, 348–349 structure identification within, technology platforms for, 8–10 CFD as, computational cellular dynamics and, 10 SBML as, Systems Biology Graphical Notation as, Systems Biology Workbench as, Systems Biology Graphical Notation (SBGN), CellDesigner and, 422–424, 432–433 components of, 424 MPF in, 424–425 process diagram for, 425 Systems Biology Mark-Up Language (SBML), 8, 395–420 BioModels database under, 411–412 CellDesigner and, 422–423, 425–429, 431–433 conversion utilities for, 410–411 evolution of, 396–398 as file format, 381 in signaling pathways, 381 goals of, 396 Level models for, 398–406 array extensions of, 407 compartments in, 402–403 diagramming in, 408 dynamic modeling in, 408 events in, 405–406 function definitions for, 401–402 hybrid modeling in, 408 object hierarchy within, 399–400 parameters within, 403, 408 reactions within, 404–405, 407 rules of, 403–404 spatial features in, 408 species in, 403, 406 units definitions for, 402, 406 vocabulary controls in, 407 LibSBML, 409 MathSBML, 395, 400–401, 410, 412–420 API command control under, 413 command summary, 414 mathematical expressions in, 400–401 model editors under, 418–420 model imports for, 414–415 names under, 415–416 simulation models for, 416–418 subsets of, 401 summary of, 413 variable scoping for, 415–416 MATLAB under, 410 model survivability from, 396 modifications to, 406–408 ODE for, 422 online tools for, 409 UML diagram, 400 workshops for, 397 XML in, 395–396 schemas for, 411 Systems Biology Workbench (SBW), 8, 422–423, 427, 431, 433 CellDesigner and, 422–423, 427 T Tandem affinity purification (TAP), 165 in large-scale protein analysis, 165–167 TAP See Tandem affinity purification TD See Transcriptional Desert TF See Transcriptional forest TFs See Transcription factors Thermodynamics, 17 TK See Transcriptional Framework TNF See Tumor necrosis factor Index TOF-MS See Mass spectrometry, time-of-flight Tradeoffs, between robustness, Transcriptional control networks, 106–119 Boltzmann factor in, 109 DNA binding sites and, 107, 109 DNA sequences and, 114–116 DPInteract and, 107 in eukaryotes, 107 evolution in, 116–117 information theoretic weight matrix and, 108–114 SVM as part of, 110–114 initiation of, 106–107 motifs in, 116–117, 119 one-class classifiers within, 119 phylogenetic footprinting and, 116–117, 119 in prokaryotes, 107 RegulonDB and, 107 SELEX method for, 107 TFs in, 106 Transcriptional Desert (TD), 96 Transcriptional forest (TF), 96 Transcriptional Framework (TK), 96 Transcriptional regulatory networks (TRNs), 124–143 enzyme databases for, 126 genomic sequencing in, 124, 126 graph theory for, 125, 131–134 integration of, 130–131, 134 ORFs in, 125 reconstruction methods for, 125–126, 128–130 in eukaryotes, 130 metabolic v., 129 in prokaryotes, 129 structural analysis of, 134–143 APL in, 134–136 “bow-tie,” 138–141 degree distribution in, 134–136 multilayer acyclic structure, 141–143 network centrality in, 136–138 scale-free networks and, 134 Transcriptional units, 86 in FANTOM 3, 97, 99 Transcription factors (TFs), 106, 133 Transcription start sites (TSSs), 78, 86 CAGE and, 92 SAGE and, 94 Transcriptome analysis, 49–53, 85 for mice, 85–100 FANTOM, 78, 85–86, 94–100 genome network analysis for, 101–102 genome technologies for, 101 Mouse Encyclopedia Project, 86–94 tiling arrays for, 100–101 TRANSFAC (data model), 487 TRANSPATH (data model), 487 TRNs See Transcriptional regulatory networks Trophoblasts, 330–331 Trypsin, 161–162 in stable isotope labeling, 170 TSSs See Transcription start sites Tumor necrosis factor (TNF), 169 2DE See dimensional electrophoresis dimensional electrophoresis (2DE), 51 U Ubiquitin, 178–179 in PPIs, 178–179 split system for, 178–179 Ultrasensitive signaling cascades, 282–296 feedback effects on, 291–296 adaptation as, 295–296 bifurcation analysis of, 292 bistability, 291–294 linear response as, 294–295 oscillations as, 296 transduction cascades and, 292 MAPK cascades and, 284, 292 mechanisms for, 286–291 cooperative binding as, 290–291 EFGR and, 288 multiple modification sites and, 288–290 sensitivity amplification as, 291 stoichiometric inhibition as, 290 substrate sequestration as, 290–291 zero-order ultrasensitivity as, 286–288 quantification methods for, 283–286 Hill coefficient in, 283–284 MCA, 285–286 UniGene, 70, 73–74 clusters, 71 gene group accession numbers for, 70, 73–74 V “Vertical point science,” 102 VisualBioMAZE, 501 W Weiner, Norbert, Wild type phage λ model, 353–354 lytic switching in, 358 X XML See EXtensible Markup Language Y Y2H See Yeast two-hybrids yeast Microarray Global Viewer (yMGV), 153 Yeast two-hybrids (Y2H), 160, 171–179 AD in, 172 alternative, 178–179 benefits/disadvantages of, 172–173 DBD in, 171–172 FPs in, 173 biological, 173 technical, 173 interactome mapping for, 173–175, 179 “many-to-many” mode for, 173 ORFs in, 173 IST in, 174 PPIs and, 160, 171–179 principles of, 171–172 541 542 Index Yeast two-hybrids (Y2H) (cont.) reverse, 175–177 dual-bait, 176 interaction-defective allele isolation with, 175 mapping interaction domains through, 175 separate-of-function alleles isolation with, 175–176 split ubiquitin system in, 178–179 three-hybrid systems and, 177–178 yMGV See yeast Microarray Global Viewer Z Zak, Daniel, 230 Zero-order kinetics, 284 Zero-order ultrasensitivity, 286–288 Goldbeter-Koshland switch in, 287 ... Stunning diversity and robustness of biological systems are the most intriguing features of living systems, and can be observed across an astonishingly broad range of species Robustness is the fundamental... of systems biology is to understand robustness and its trade-offs in biological systems and the principle behind them (15) Why is robustness so important? First, it is a feature that is observed... specific guidance from the cytoskeleton Second, system dynamics need to be understood Understanding the dynamics of the system is an essential aspect of study in systems biology This requires integrative