Computational modeling of gene regulatory networks a primer

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1. Verfasser: Bolouri, Hamid (VerfasserIn)
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Sprache:English
Veröffentlicht: London Imperial College Press 2008
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adam_text COMPUTATIONAL MODELING OF GENE REGULATORY NETWORKS * A PRIMER HAMID BOLOURI IMPERIAL COLLEGE PRESS CONTENTS 1. INTRODUCTION 1 THE INCREASING ROLE OF COMPUTATIONAL ANALYSIS IN BIOLOGY 1 WHAT THIS BOOK TRIES TO ACHIEVE 3 WHO SHOULD READ THIS BOOK 4 HOW THIS BOOK IS ORGANIZED 6 ACKNOWLEDGMENTS 7 FEEDBACK 7 2. WHAT IS A SYSTEM, AND WHY SHOULD WE CARE? * 9 LINEARITY VERSUS NONLINEARITY 9 NONLINEAR SYSTEMS 13 NONLINEAR SYSTEMS ARE THE NORM, NOT THE EXCEPTION, IN BIOLOGY 14 3. WHAT MODELS CAN AND CANNOT PREDICT 17 INTERPOLATION VERSUS EXTRAPOLATION 17 ITERATIVE MODEL REFINEMENT BY EXPERIMENTAL FALSIFICATION OF MODEL EXTRAPOLATIONS 21 THE IMPORTANCE OF REMEMBERING THE LIMITATIONS OF DATA 22 CROSS-VALIDATION 23 FUNCTION APPROXIMATION VERSUS CLASSIFICATION 25 APPENDIX: A MODEL OF BIPHASIC KINETICS 26 VIII COMPUTATIONAL MODELING OF GENE REGULATORY NETWORKS * A PRIMER 4. WHY MAKE COMPUTATIONAL MODELS OF GENE REGULATORY NETWORKS? 29 WHAT IS A MODEL? 29 WHAT IS THE GOAL OF GRN MODELING? 31 WHY MAKE COMPUTATIONAL MODELS OF GRNS? 32 SERENDIPITOUS BENEFITS OF COMPUTATIONAL GRN MODELING 33 SOME PITFALLS OF MODELING 34 GOOD PRACTICE GUIDELINES 35 APPENDIX: WORKING DEFINITIONS OF GENES AND GENE REGULATORY NETWORKS 36 5. GRAPHICAL REPRESENTATIONS OF GENE REGULATORY NETWORKS 39 DESIRABLE FEATURES OF COMPUTATIONAL GRN REPRESENTATIONS 39 GRAPHICAL REPRESENTATION OF GRN ACTIVITY IN MULTIPLE COMPARTMENTS 43 COMPUTATIONAL NETWORK BUILDING, EDITING, AND TOPOLOGICAL ANALYSIS 46 6. IMPLICIT MODELING VIA INTERACTION NETWORK MAPS 49 DATA INTERPRETATION THROUGH IMPLICIT MODELING 49 GLOBAL MOLECULAR INTERACTION MAPS * GUILT BY ASSOCIATION 50 WHY DO WE NEED GLOBAL MOLECULAR INTERACTION MAPS? 53 EXAMPLE USES OF INTERACTION MAPS AS PREDICTIVE MODELS 54 7. THE BIOCHEMICAL BASIS OF GENE REGULATION 61 THE PROBABILITY OF A CHEMICAL REACTION 61 A SIMPLE METHOD FOR MODELING STOCHASTIC MOLECULAR REACTION EVENTS 63 CHEMICAL KINETICS IN CELLS ARE DIFFERENT FROM IN VITRO KINETICS 65 COMPARED TO TRANSCRIPTION, MOST SIGNALING EVENTS ARE INSTANTANEOUS 66 HOW TRANSCRIPTION FACTORS FIND THEIR TARGETS ON DNA 67 DNA BENDING AND LOOPING BY TRANSCRIPTION FACTORS 70 SPATIAL LOCALIZATION: MULTI-COMPARTMENT MODELING 71 CONTENTS IX MORPHOGEN GRADIENTS 72 APPENDIX: STOCHASTIC SIMULATION USING GILLESPIE S ALGORITHM 73 8. A SINGLE-CELL MODEL OF TRANSCRIPTIONAL REGULATION 77 MODELING STRATEGY 77 MODELING FRAMEWORK AND NOTATION 78 A SINGLE-CELL STOCHASTIC MODEL OF TRANSCRIPTIONAL REGULATION 79 RECRUITMENT OF RNA POLYMERASE II COMPLEX AND TRANSCRIPTION INITIATION 82 APPENDIX: SIMULATION OF THE DISTRIBUTION OF GENE EXPRESSION LEVELS IN A POPULATION OF GENETICALLY IDENTICAL CELLS 89 9. SIMPLIFIED MODELS: MASS-ACTION KINETICS 99 WHY MODEL WITH MASS-ACTION KINETICS? 99 THE FUNDAMENTALS OF ORDINARY DIFFERENTIAL EQUATIONS (ODES) 100 STEADY STATES 103 AVERAGE PROMOTER OCCUPANCY BY A SINGLE TRANSCRIPTION FACTOR 104 PROMOTER OCCUPANCY BY TWO OR MORE FACTORS 105 A TWO-STEP KINETIC MODEL OF MRNA AND PROTEIN CONCENTRATION 107 MRNA AND PROTEIN LEVELS AT STEADY STATE 109 PROMOTER OCCUPANCY AS A FUNCTION OF REGULATOR CONCENTRATION 109 ANALYTICAL SOLUTION OF MRNA AND PROTEIN TIME-COURSE KINETICS FOR GENES REGULATED BY POSTTRANSCRIPTIONALLY ACTIVATED FACTORS 110 THE TIME-COURSE BEHAVIOR OF GENES REGULATED BY OTHER GENES 112 THE BOOLEAN APPROXIMATION TO TRANSCRIPTION KINETICS 114 IN THE ABSENCE OF FEEDBACK, TRANSCRIPTION FACTORS IN ANIMALS DO NOT REACH STEADY STATE 115 POSITIVE AND NEGATIVE FEEDBACK LOOPS CAN DRIVE GENE EXPRESSION TO FIXED STEADY-STATE LEVELS 117 GENE EXPRESSION AS A FUNCTION OF DNA-BOUND REGULATOR ACTIVITY 117 X COMPUTATIONAL MODELING OF GENE REGULATORY NETWORKS * A PRIMER APPENDIX A: ODE MODELING WITH BERKELEY MADONNA 119 APPENDIX B: DERIVATION OF MATHEMATICAL EXPRESSIONS FOR MRNA AND PROTEIN LEVELS AS A FUNCTION OF CHANGING OCCUPANCY LEVELS 120 APPENDIX C: TIME TO STEADY STATE FOR GENES NOT REGULATED BY FEEDBACK 122 10. SIMPLIFIED MODELS: BOOLEAN AND MULTI-VALUED LOGIC 123 BACKGROUND 123 DISCRETE-VARIABLE PIECEWISE LINEAR ODES 125 MULTI-VALUED LOGIC NETWORKS 129 IMPLICIT-TIME LOGIC NETWORKS (A.K.A. KINETIC LOGIC) 132 LEARNING DISCRETE LOGIC MODELS DIRECTLY FROM DATA 135 LINEAR ODE MODELS OF TRANSCRIPTIONAL REGULATION 136 PROCESS ALGEBRAS 139 APPENDIX: LOGIC SIMULATION MODEL FILES 140 11. SIMPLIFIED MODELS: BAYESIAN NETWORKS 143 A PREVIEW 145 PROBABILITIES: A BRIEF REVIEW 146 CONTINUOUS AND DISCRETE PROBABILITY DISTRIBUTIONS 148 THE THEORETICAL FOUNDATION OF BNS: CONDITIONAL PROBABILITIES 149 MAKING PREDICTIONS WITH A GIVEN BN 151 MODELING NETWORKS WITH FEEDBACK AS DYNAMIC BAYESIAN NETWORKS 154 CONSTRUCTING BNS DIRECTLY FROM DATA 156 CAUSALITY IN BNS 161 COMPUTATIONAL EFFICIENCY IN BNS 162 CURRENT LIMITATIONS OF BAYESIAN NETWORKS 163 RESOURCES FOR BNS 164 APPENDIX: EXPLORING BNS WITH HUGIN 165 CONTENTS XI 12. THE RELATIONSHIP BETWEEN LOGIC AND BAYESIAN NETWORKS 167 NOISY LOGIC NETWORKS 167 PROBABILISTIC BOOLEAN NETWORKS 169 LEARNING PBNS FROM DATA 171 SOME USEFUL PROPERTIES OF PBNS 172 13. NETWORK INFERENCE IN PRACTICE 175 A SUMMARY OF THE GENERAL APPROACH TO NETWORK RECONSTRUCTION 175 LEARNING LOGIC MODELS FROM GENE EXPRESSION DATA ALONE 178 LEARNING CONTINUOUS-VALUED NETWORK MODELS FROM EXPRESSION DATA 182 NETWORK STRUCTURE BUILDING BY DATA INTEGRATION 184 14. SEARCHING DNA SEQUENCES FOR TRANSCRIPTION FACTOR BINDING SITES 189 CONSENSUS SEQUENCES 189 POSITION WEIGHT MATRICES 191 VISUALIZING PWMS WITH SEQUENCE LOGOS 194 A TAXONOMY OF TFBS PREDICTION ALGORITHMS 196 RESOURCES FOR TFBS PREDICTION 201 SOME GOOD PRACTICE GUIDELINES 202 MEASURING THE PERFORMANCE OF BINDING SITE PREDICTION ALGORITHMS 204 EXTRACTING PREDICTED TFBSS FROM CHLP-CHIP DATA 206 APPENDIX: DNA SEQUENCE PROCESSING 211 15. MODEL SELECTION THEORY 213 FITTING ERROR VERSUS GENERALIZATION ERROR 213 MODEL MISSPECIFICATION 214 MODEL INVALIDATION 215 XII COMPUTATIONAL MODELING OF GENE REGULATORY NETWORKS * A PRIMER MODEL SELECTION CRITERIA 216 HOW TO CALCULATE THE LOG-LIKELIHOOD VALUE FOR A REGRESSION MODEL 219 PARAMETER COUNTS OF COMMON MODELING FRAMEWORKS 221 THE EFFECT OF FUNCTION COMPLEXITY 222 MULTI-MODEL AVERAGING 223 OTHER APPROACHES TO MODEL REFINEMENT 224 16. SIMPLIFIED MODELS * GRN STATE SIGNATURES IN DATA 225 PRINCIPAL COMPONENT ANALYSIS 226 NONLINEAR PC A 232 MULTI-DIMENSIONAL SCALING (MDS) 235 PARTIAL LEAST SQUARES (PLS) 237 THE IMPLICIT APPROACH TO PATTERN DETECTION IN COMPLEX DATA 237 APPENDIX: STEP-BY-STEP EXAMPLE PCA TRANSFORMATIONS 239 17. SYSTEM DYNAMICS 243 TRANSIENTS AND STEADY STATES , 243 PHASE PORTRAITS 245 PARAMETER ANALYSIS 249 PARAMETER OPTIMIZATION AND THE EVOLUTION OF OPTIMAL DYNAMICS 252 BISTABILITY THROUGH MUTUAL INHIBITION 254 NEGATIVE AUTO-REGULATION 255 MIXED POSITIVE AND NEGATIVE FEEDBACK 258 APPENDIX: ANALYZING FEEDBACK DYNAMICS 260 18. ROBUSTNESS ANALYSIS 265 ROBUSTNESS AND SENSITIVITY 265 PERTURBATIONS IN SYSTEM STATE VARIABLES VERSUS PERTURBATIONS IN SYSTEM PARAMETERS 266 CONTENTS XIII FAILURE TOLERANCE VERSUS GRACEFUL DEGRADATION 266 GLOBAL AND LOCAL PERSPECTIVES 268 LOCAL SENSITIVITY ANALYSIS 268 GLOBAL SENSITIVITY ANALYSIS 270 THE ROLE OF NETWORK TOPOLOGY IN ROBUSTNESS 273 EVOLUTION OF ROBUSTNESS 275 ROBUSTNESS TO TRANSCRIPTIONAL NOISE 277 CONTEXT AND COMPLETENESS OF MODELS 277 19. GRN MODULES AND BUILDING BLOCKS 279 HIERARCHICAL MODULARITY IN ENGINEERED SYSTEMS 279 ORGANIZATIONAL PRINCIPLES IN GRNS 281 NETWORK MOTIFS IN GRNS 283 FUNCTIONAL BUILDING BLOCKS 288 USING NETWORK MOTIFS AND FUNCTIONAL BUILDING BLOCKS TO DECODE GRNS 290 20. NOTES ON DATA PROCESSING FOR GRN MODELING 293 WHAT TYPE OF DATA IS BEST FOR MODELING? 293 BEWARE OF THE SIDE-EFFECTS OF THE METHODS USED TO COLLECT DATA 294 HOW MANY TIME POINTS ARE SUFFICIENT FOR MODELING DYNAMICS? 295 IN VIVO VERSUS EX VIVO AND IN VITRO DATA 296 USING MEANINGFUL UNITS TO QUANTIFY DATA 297 MISINTERPRETING DATA 297 21. APPLICATIONS OF COMPUTATIONAL GRN MODELING 299 OVERVIEW 299 GRN MODELING CHALLENGES IN MEDICAL SYSTEMS BIOLOGY 301 MODELING HIERARCHICAL, DISTRIBUTED PROCESSING IN THE IMMUNE SYSTEM 305 XIV COMPUTATIONAL MODELING OF GENE REGULATORY NETWORKS * A PRIMER 22. QUOVADIS 311 THE US$1000 GENOME AND ITS CHALLENGES 311 SINGLE-CELL BIOLOGY 313 MULTI-SCALE MODELING 315 SOFTWARE ENGINEERING CHALLENGES 316 BECOMING BILINGUAL 318 MOLECULAR BIOLOGY IS STILL IN THE DISCOVERY PHASE 319 INDEX 321
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spelling Bolouri, Hamid Verfasser aut
Computational modeling of gene regulatory networks a primer Hamid Bolouri
London Imperial College Press 2008
XIV, 326 S. Ill., graph. Darst.
txt rdacontent
n rdamedia
nc rdacarrier
Computersimulation swd
Genregulation swd
Mathematisches Modell
Biochemistry Mathematical models
Computer Simulation
Gene Expression Regulation
Gene Regulatory Networks
Genomics
Molecular Biology
Molecular biology Mathematical models
HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018615142&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle Bolouri, Hamid
Computational modeling of gene regulatory networks a primer
Computersimulation swd
Genregulation swd
Mathematisches Modell
Biochemistry Mathematical models
Computer Simulation
Gene Expression Regulation
Gene Regulatory Networks
Genomics
Molecular Biology
Molecular biology Mathematical models
title Computational modeling of gene regulatory networks a primer
title_auth Computational modeling of gene regulatory networks a primer
title_exact_search Computational modeling of gene regulatory networks a primer
title_full Computational modeling of gene regulatory networks a primer Hamid Bolouri
title_fullStr Computational modeling of gene regulatory networks a primer Hamid Bolouri
title_full_unstemmed Computational modeling of gene regulatory networks a primer Hamid Bolouri
title_short Computational modeling of gene regulatory networks
title_sort computational modeling of gene regulatory networks a primer
title_sub a primer
topic Computersimulation swd
Genregulation swd
Mathematisches Modell
Biochemistry Mathematical models
Computer Simulation
Gene Expression Regulation
Gene Regulatory Networks
Genomics
Molecular Biology
Molecular biology Mathematical models
topic_facet Computersimulation
Genregulation
Mathematisches Modell
Biochemistry Mathematical models
Computer Simulation
Gene Expression Regulation
Gene Regulatory Networks
Genomics
Molecular Biology
Molecular biology Mathematical models
url http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018615142&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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