Metabolic engineering concepts and applications : Volume 13a

929 Pages·2021·17.967 MB·English

by  G. Stephanopoulos, Sang Yup Lee, Jens Nielsen

MetabolicEngineering
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Metabolic Engineering
ConceptsandApplications
Editedby
SangYupLee
JensNielsen
GregoryStephanopoulos
Volume13a
Metabolic Engineering
ConceptsandApplications
Editedby
SangYupLee
JensNielsen
GregoryStephanopoulos
Volume13b
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vii
Contents
Volume13a
Preface xv
PartI Concepts 1
1 MetabolicEngineeringPerspectives 3
NianLiuandGregoryStephanopoulos
1.1 HistoryandOverviewofMetabolicEngineering 3
1.2 UnderstandingCellularMetabolismandPhysiology 5
1.2.1 ComputationalMethodsinUnderstandingMetabolism 6
1.2.2 ExperimentalMethodsinUnderstandingMetabolism 7
1.3 GeneralApproachestoMetabolicEngineering 9
1.3.1 RationalMetabolicEngineering 10
1.3.2 CombinatorialMetabolicEngineering 12
1.3.3 SystemsMetabolicEngineering 14
1.4 HostOrganismSelection 15
1.5 SubstrateConsiderations 15
1.6 MetabolicEngineeringandSyntheticBiology 16
1.7 TheFutureofMetabolicEngineering 17
References 19
2 Genome-ScaleModels:TwoDecadesofProgressanda2020
Vision 23
BernhardO.Palsson
2.1 Introduction 23
2.2 FluxBalanceAnalysis 23
2.2.1 DynamicMassBalances 23
2.2.2 AnalogytoDerivingEnzymaticRateEquations 25
2.2.3 FormulatingFluxBalancesattheGenome-Scale 25
2.2.4 ConstrainedOptimization 26
2.2.5 Principles 26
2.2.6 AdditionalConstraints 27
viii Contents
2.2.7 Flux–ConcentrationDuality 28
2.2.8 Recap 28
2.3 NetworkReconstruction 30
2.3.1 AssemblingtheReactome 30
2.3.2 BasicPrinciplesofNetworkReconstruction 30
2.3.3 Curation 32
2.3.4 GEMsHaveaGenomicBasis 32
2.3.5 ComputationalQueries 32
2.3.6 ScopeExpansion 33
2.3.7 KnowledgeBases 35
2.3.8 AvailabilityofGEMs 35
2.3.9 Recap 35
2.4 BriefHistoryoftheGEMforE.coli 36
2.4.1 Origin 36
2.4.2 ModelOrganism 36
2.4.3 KeyPredictions 38
2.4.4 DesignAlgorithms 38
2.4.5 ScopeExpansions 41
2.4.6 Recap 42
2.5 FromMetabolismtotheProteome 42
2.5.1 MEModels 42
2.5.2 CapabilitiesofMEModels 43
2.5.2.1 Growth-CoupledMetabolicDesignsCanBeReproducedin
GEMs 43
2.5.2.2 MEModelsCanReflectPropertiesoftheMetalloproteome 44
2.5.2.3 MEModelsCanComputetheBiomassObjectiveFunction 44
2.5.2.4 ComputingStresses 46
2.5.3 Recapitulation 49
2.6 CurrentDevelopments 50
2.6.1 Kinetics 50
2.6.2 TranscriptionalRegulation 51
2.6.2.1 iModulons 52
2.6.2.2 Activities 52
2.6.3 ProteinStructures 55
2.7 BroaderPerspectives 56
2.7.1 DistalCausation 56
2.7.2 ContextualizationofGEMsWithinWorkflows 57
2.8 WhatDoestheFutureLookLikeforGEMs? 59
Disclaimer 62
Acknowledgments 63
References 63
3 QuantitativeMetabolicFluxAnalysisBasedonIsotope
Labeling 73
WolfgangWiechertandKatharinaNöh
3.1 Introduction 73
Contents ix
3.1.1 WhatMetabolicFluxAnalysisIsAbout 73
3.1.2 TheVariantsof13C-MFA 76
3.2 AToyExampleIllustratestheBasicPrinciples 77
3.2.1 Fluxomics:MoreThanJustaBranchofMetabolomics 77
3.2.2 IsotopeLabeling:TheKeytoMetabolicFluxes 79
3.2.3 FromtheDatatotheIntracellularFluxes 82
3.2.4 INST-13C-MFA:MetabolicStationary,butIsotopically
Nonstationary 83
3.2.5 FromMeasurementstoFluxEstimates:ParameterFitting 84
3.2.6 FluxEstimatesHaveConfidenceBounds:StatisticalAnalysis 86
3.2.7 TheClassicalApproachatMetabolicandIsotopicStationaryState 90
3.2.8 AnAdditionalSourceofInformation:CarbonAtomTransitions 91
3.2.9 InputLabelingDesign:HowInformativeCananExperimentBe
Made? 93
3.2.10 TheIsotopomersofaSingleMetaboliteCanBeaRichSourceof
Information 94
3.2.11 BidirectionalReactionSteps:MoreThanJustNuisanceFactors 95
3.2.12 IsotopomerFractionsCannotBeMeasuredComprehensively 96
3.3 LessonsLearnedfromtheExample 97
3.3.1 Definitionof13C-MFARevisited 97
3.3.2 StatisticalEvaluationandOptimalExperimentalDesign 99
3.4 HowtoConfigureanIsotopeLabelingExperiment 100
3.4.1 ModelingandSimulationofIsotopeLabelingExperiments 101
3.4.2 MetabolicNetworkSpecification 101
3.4.3 AtomTransitionNetworkSpecification 103
3.4.4 InputLabelingComposition 104
3.4.5 MeasurementSpecification 106
3.4.6 FluxConstraints 107
3.4.7 InSilicoExperimentalILEDesign 108
3.5 PuttingTheoryintoPractice 108
3.5.1 ARecipeHowtoStart 108
3.5.2 MetabolicandIsotopicStationarity 110
3.5.3 MeasuringExtracellularFluxes 111
3.5.4 AdministeringLabeledSubstrate(s) 112
3.5.5 Metabolomics:Sampling,SamplePreparation,andAnalytical
Procedures 113
3.5.6 AdjustingLabelingEnrichmentsforIsotopicSteadyState
Approximation 115
3.5.7 CorrectingLabelingEnrichmentsforNaturalIsotope
Abundance 116
3.5.8 SimulationofLabelingDataandFluxEstimation 117
3.5.9 DelicaciesofINST-13C-MFA 123
3.6 FutureChallengesof13C-MFA 124
Acknowledgments 125
Abbreviations 125
References 126
x Contents
4 ProteomeConstraintsinGenome-ScaleModels 137
YuChen,JensNielsen,andEduardJ.Kerkhoven
4.1 Introduction 137
4.2 CellularConstraints 137
4.3 FormulationofProteomeConstraints 139
4.3.1 Coarse-GrainedIntegrationofProteomeConstraints 139
4.3.2 Fine-TunedIntegrationofProteomeConstraints 144
4.4 Perspectives 150
References 151
5 KineticModelsofMetabolism 153
HongzhongLu,YuChen,JensNielsen,andEduardJ.Kerkhoven
5.1 Introduction 153
5.2 DefinitionofEnzymeKinetics 153
5.2.1 Michaelis–MentenFormula 153
5.3 FactorsAffectingIntracellularEnzymeKinetics 155
5.4 KineticModel:DefinitionandScope 156
5.4.1 WhatIsaKineticModel? 156
5.4.2 ScopeofKineticModels 156
5.4.3 HowtoBuildaFunctionalKineticModel? 157
5.5 MainMathematicalExpressionsinDescriptionofReactionRates 158
5.5.1 MechanisticRateExpressions 158
5.6 ApproximativeRateExpressions 159
5.7 ApproachestoAssignParametersintheRateExpressions 160
5.7.1 DirectMeasurementsofKineticParametersinEnzymeAssays 161
5.7.2 QueryingDatabases 161
5.7.3 InferringfromMeasuredFluxes 162
5.7.4 ParametersInferenceUsingtheStatisticalAnalysis 163
5.8 Applications 166
5.9 Perspectives 167
References 168
6 MetabolicControlAnalysis 171
DavidA.Fell
6.1 TheMetabolicEngineeringContextofMetabolicControl
Analysis 171
6.2 MCATheory 174
6.2.1 MetabolicSteadyState 174
6.2.2 FluxControlCoefficients 175
6.2.3 ExamplesoftheFlux–EnzymeRelationship 176
6.2.4 FluxSummationTheorem 178
6.2.5 ConcentrationControlCoefficients 179
6.2.6 LinkingControlCoefficientstoEnzymeProperties 181
6.2.6.1 EnzymeRateEquationsandElasticityCoefficients 181
6.2.6.2 ElasticitiesandControlCoefficients 184
6.2.6.3 BlockCoefficientsandTop-DownAnalysis 186
6.2.7 FeedbackInhibition 186