Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, and Ralf Herwig Systems Biology Related Titles Helms, V. Principles of Computational Cell Biology FromProteinComplexestoCellularNetworks 2008 Softcover ISBN:978-3-527-31555-0 Baxevanis, A.D., Ouellette, B. F.F. (eds.) Bioinformatics APracticalGuidetotheAnalysisofGenesandProteins 560pages 2004 Hardcover ISBN:978-0-471-47878-2 Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, and Ralf Herwig Systems Biology A Textbook TheAuthors AllbookspublishedbyWiley-VCHarecarefully produced.Nevertheless,authors,editors,and Prof.EddaKlipp publisherdonotwarranttheinformationcontained Humboldt-UniversitätBerlin inthesebooks,includingthisbook,tobefreeof InstitutfürBiologie errors.Readersareadvisedtokeepinmindthat TheoretischeBiophysik statements,data,illustrations,proceduraldetailsor Invalidenstr.42 otheritemsmayinadvertentlybeinaccurate. 10115Berlin LibraryofCongressCardNo.: appliedfor Dr.WolframLiebermeister Humboldt-UniversitätBerlin BritishLibraryCataloguing-in-PublicationData InstitutfürBiologie Acataloguerecordforthisbookisavailablefromthe TheoretischeBiophysik BritishLibrary. Invalidenstr.42 10115Berlin Bibliographicinformationpublishedby theDeutscheNationalbibliothek Dr.ChristophWierling TheDeutscheNationalbibliothekliststhis publicationintheDeutscheNationalbibliografie; MPIfürMolekulareGenetik detailedbibliographicdataareavailableonthe Ihnestr.73 14195Berlin Internetathttp://dnb.d-nb.de. Germany #2009WILEY-VCHVerlagGmbH&Co.KGaA, Dr.AxelKowald Weinheim ProtagenAG Allrightsreserved(includingthoseoftranslationinto Otto-Hahn-Str.15 otherlanguages).Nopartofthisbookmaybe 44227Dortmund reproducedinanyform–byphotoprinting, Prof.HansLehrach microfilm,oranyothermeans–nortransmittedor translatedintoamachinelanguagewithoutwritten MPIfürMolekulareGenetik permissionfromthepublishers.Registerednames, Ihnestr.73 trademarks,etc.usedinthisbook,evenwhennot 14195Berlin specificallymarkedassuch,arenottobeconsidered Germany unprotectedbylaw. Prof.RalfHerwig Typesetting ThomsonDigital,Noida,India MPIfürMolekulareGenetik Printing StraussGmbH,Mörlenbach Ihnestr.73 Binding Litges&DopfGmbH,Heppenheim 14195Berlin CoverDesign Adam-Design,Weinheim Germany PrintedintheFederalRepublicofGermany Cover Printedonacid-freepaper Thecoverpictureswereprovidedwithkindpermission bySantiagoOrtizandDr.MichaelErlowitz ISBN:978-3-527-31874-2 V Contents Preface XVII PartOne IntroductiontoSystemsBiology 1 1 Introduction 3 1.1 BiologyinTimeandSpace 3 1.2 ModelsandModeling 4 1.2.1 WhatisaModel? 5 1.2.2 PurposeandAdequatenessofModels 5 1.2.3 AdvantagesofComputationalModeling 6 1.3 BasicNotionsforComputationalModels 7 1.3.1 ModelScope 7 1.3.2 ModelStatements 8 1.3.3 SystemState 8 1.3.4 Variables,Parameters,andConstants 8 1.3.5 ModelBehavior 9 1.3.6 ModelClassification 9 1.3.7 SteadyStates 9 1.3.8 ModelAssignmentisnotUnique 10 1.4 DataIntegration 11 1.5 Standards 12 References 12 2 ModelingofBiochemicalSystems 13 2.1 KineticModelingofEnzymaticReactions 13 2.1.1 TheLawofMassAction 14 2.1.2 ReactionKineticsandThermodynamics 15 2.1.3 Michaelis–MentenKinetics 18 2.1.3.1 HowtoDeriveaRateEquation 19 2.1.3.2 ParameterEstimationandLinearizationoftheMichaelis–Menten Equation 20 2.1.3.3 TheMichaelis–MentenEquationforReversibleReactions 22 SystemsBiology:ATextbook.EddaKlipp,WolframLiebermeister,ChristophWierling,AxelKowald, HansLehrach,andRalfHerwig Copyright(cid:1)2009WILEY-VCHVerlagGmbH&Co.KGaA,Weinheim ISBN:978-3-527-31874-2 VI Contents 2.1.4 RegulationofEnzymeActivitybyEffectors 22 2.1.4.1 SubstrateInhibition 25 2.1.4.2 BindingofLigandstoProteins 26 2.1.4.3 PositiveHomotropicCooperativityandtheHillEquation 27 2.1.4.4 TheMonod–Wyman–ChangeuxModelforSigmoid Kinetics 28 2.1.5 GeneralizedMassActionKinetics 29 2.1.6 ApproximateKineticFormats 30 2.1.7 ConvenienceKinetics 30 2.2 StructuralAnalysisofBiochemicalSystems 31 2.2.1 SystemsEquations 31 2.2.2 InformationEncodedintheStoichiometricMatrixN 34 2.2.3 ElementaryFluxModesandExtremePathways 36 2.2.3.1 FluxCone 37 2.2.4 ConservationRelations:NullSpaceofNT 39 2.3 KineticModelsofBiochemicalSystems 42 2.3.1 DescribingDynamicswithODEs 42 2.3.1.1 Notations 43 2.3.1.2 LinearizationofAutonomousSystems 44 2.3.1.3 SolutionofLinearODESystems 45 2.3.1.4 StabilityofSteadyStates 46 2.3.1.5 GlobalStabilityofSteadyStates 49 2.3.1.6 LimitCycles 49 2.3.2 MetabolicControlAnalysis 51 2.3.2.1 TheCoefficientsofControlAnalysis 52 2.3.2.2 TheElasticityCoefficients 52 2.3.2.3 ControlCoefficients 55 2.3.2.4 ResponseCoefficients 55 2.3.2.5 MatrixRepresentationoftheCoefficients 55 2.3.2.6 TheTheoremsofMetabolicControlTheory 56 2.3.2.7 TheSummationTheorems 56 2.3.2.8 TheConnectivityTheorems 58 2.3.2.9 DerivationofMatrixExpressionsforControlCoefficients 59 2.4 ToolsandDataFormatsforModeling 63 2.4.1 SimulationTechniques 64 2.4.1.1 PetriNets 64 2.4.1.2 CellularAutomata 65 2.4.2 SimulationTools 65 2.4.2.1 CellDesigner 66 2.4.2.2 COPASI 67 2.4.2.3 PyBioS 68 2.4.3 DataFormats 70 2.4.3.1 SystemsBiologyMarkupLanguage 70 2.4.3.2 BioPAX 73 2.4.3.3 SystemsBiologyGraphicalNotation 73 Contents VII 2.4.3.4 StandardsforSystemsBiology 74 2.4.4 DataResources 75 2.4.4.1 PathwayDatabases 76 2.4.4.2 DatabasesofKineticData 77 2.4.4.3 ModelDatabases 77 References 79 3 SpecificBiochemicalSystems 83 3.1 MetabolicSystems 83 3.1.1 BasicElementsofMetabolicModeling 84 3.1.2 ToyModelofUpperGlycolysis 85 3.1.3 ThreonineSynthesisPathwayModel 88 3.2 SignalingPathways 91 3.2.1 Introduction 92 3.2.2 FunctionandStructureofIntra-andIntercellular Communication 92 3.2.3 Receptor–LigandInteractions 93 3.2.4 StructuralComponentsofSignalingPathways 96 3.2.4.1 Gproteins 96 3.2.4.2 SmallGproteins 99 3.2.4.3 PhosphorelaySystems 100 3.2.4.4 MAPKinaseCascades 102 3.2.4.5 Jak/StatPathways 106 3.2.5 Signaling–DynamicandRegulatoryFeatures 106 3.2.5.1 QuantitativeMeasuresforPropertiesofSignalingPathways 107 3.2.5.2 CrosstalkinSignalingPathways 109 3.3 TheCellCycle 111 3.3.1 StepsintheCycle 114 3.3.2 MinimalCascadeModelofaMitoticOscillator 115 3.3.3 ModelsofBuddingYeastCellCycle 117 3.3.4 ModelingNucleo/CytoplasmaticCompartmentalization 119 3.4 SpatialModels 121 3.4.1 TypesofSpatialModels 122 3.4.1.1 CompartmentModelsandPartialDifferentialEquations 122 3.4.1.2 StochasticModels 123 3.4.1.3 CellularAutomata 123 3.4.2 CompartmentModels 123 3.4.3 Reaction–DiffusionSystems 125 3.4.3.1 TheDiffusionEquation 125 3.4.3.2 SolutionsoftheDiffusionEquation 126 3.4.3.3 Reaction–DiffusionEquation 127 3.4.4 PatternFormationinTissueDevelopment 128 3.4.5 SpontaneousPatternFormation 130 3.5 Apoptosis 132 3.5.1 MolecularBiologyofApoptosis 132 VIII Contents 3.5.2 ModelingofApoptosis 135 References 142 4 ModelFitting 147 4.1 DataforSmallMetabolicandSignalingSystems 147 4.1.1 DatabasesforKineticModeling 148 4.1.2 MeasuringPromoterActivitiesUsingGFPReporterGenes 150 4.2 ParameterEstimation 152 4.2.1 Regression 153 4.2.2 Estimators 153 4.2.2.1 MethodofLeastSquaresandMaximum-Likelihood Estimation 155 4.2.3 Identifiability 155 4.2.4 Bootstrapping 157 4.2.5 Crossvalidation 158 4.2.6 BayesianParameterEstimation 159 4.2.7 LocalandGlobalOptimization 160 4.2.7.1 LocalOptimization 161 4.2.7.2 GlobalOptimization 161 4.2.7.3 SamplingMethods 162 4.2.7.4 GeneticAlgorithms 163 4.3 ReductionandCouplingofModels 164 4.3.1 ModelSimplification 164 4.3.2 TacitModelAssumptions 166 4.3.3 ReductionofFastProcesses 167 4.3.3.1 ResponseTime 167 4.3.3.2 Time-ScaleSeparation 167 4.3.4 GlobalModelReduction 170 4.3.4.1 LinearizedBiochemicalModels 171 4.3.4.2 LinearRelaxationModes 171 4.3.5 CoupledSystemsandEmergentBehavior 172 4.3.6 ModelingofCoupledSystems 174 4.3.6.1 Bottom-UpandTop-DownModeling 174 4.3.6.2 ModelingtheSystemBoundary 175 4.3.6.3 CouplingofSubmodels 175 4.3.6.4 ModelMerging 175 4.4 ModelSelection 176 4.4.1 WhatisaGoodModel? 177 4.4.2 StatisticalTestsandModelSelection 178 4.4.3 Maximum-LikelihoodEstimationandw2-Test 180 4.4.4 Overfitting 181 4.4.5 LikelihoodRatioTest 182 4.4.6 SelectionCriteria 183 4.4.7 BayesianModelSelection 184 4.4.8 CycleofExperimentsandModeling 186 Contents IX 4.4.9 ModelsareGrowinginComplexity 186 References 189 5 AnalysisofHigh-ThroughputData 193 5.1 High-ThroughputExperiments 193 5.1.1 DNAArrayPlatforms 193 5.1.2 PlatformComparison 196 5.1.3 NextGenerationSequencing 196 5.1.4 ImageAnalysisandDataQualityControl 198 5.1.4.1 GridFinding 198 5.1.4.2 SpotQuantification 200 5.1.4.3 SignalValidity 200 5.1.5 Preprocessing 202 5.1.5.1 GlobalMeasures 203 5.1.5.2 LinearModels 203 5.1.5.3 NonlinearandSpatialEffects 204 5.1.5.4 OtherApproaches 204 5.2 AnalysisofGeneExpressionData 205 5.2.1 PlanningandDesigningExperimentsforCase-ControlStudies 205 5.2.2 TestsforDifferentialExpression 206 5.2.2.1 DNAArrays 206 5.2.2.2 NextGenerationSequencing 209 5.2.3 MultipleTesting 209 5.2.4 ROCCurveAnalysis 211 5.2.5 ClusteringAlgorithms 213 5.2.5.1 HierarchicalClustering 215 5.2.5.2 Self-OrganizingMaps(SOMs) 218 5.2.5.3 K-Means 218 5.2.6 ClusterValidation 220 5.2.7 OverrepresentationandEnrichmentAnalyses 223 5.2.8 ClassificationMethods 226 5.2.8.1 SupportVectorMachines 227 5.2.8.2 OtherApproaches 229 References 232 6 GeneExpressionModels 235 6.1 MechanismsofGeneExpressionRegulation 235 6.1.1 Transcription-FactorInitiatedGeneRegulation 235 6.1.2 GeneralPromoterStructure 237 6.1.3 PredictionandAnalysisofPromoterElements 239 6.1.3.1 Sequence-BasedAnalysis 239 6.1.3.2 ApproachesthatIncorporateAdditionalInformation 241 6.1.4 PosttranscriptionalRegulationThroughmicroRNAs 243 6.1.4.1 IdentificationofmicroRNAsintheGenomeSequence 245 6.1.4.2 MicroRNATargetPrediction 246