Computational modeling of gene regulatory networks a primer
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Imperial College Press
2008
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007 | t | ||
008 | 091006s2008 ad|| |||| 00||| eng d | ||
020 | |a 9781848162204 |9 978-1-84816-220-4 | ||
020 | |a 9781848162211 |9 978-1-84816-221-1 | ||
035 | |a (OCoLC)228372433 | ||
035 | |a (DE-599)BVBBV035755161 | ||
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100 | 1 | |a Bolouri, Hamid |e Verfasser |4 aut | |
245 | 1 | 0 | |a Computational modeling of gene regulatory networks |b a primer |c Hamid Bolouri |
264 | 1 | |a London |b Imperial College Press |c 2008 | |
300 | |a XIV, 326 S. |b Ill., graph. Darst. | ||
336 | |b txt |2 rdacontent | ||
337 | |b n |2 rdamedia | ||
338 | |b nc |2 rdacarrier | ||
650 | 7 | |a Computersimulation |2 swd | |
650 | 7 | |a Genregulation |2 swd | |
650 | 4 | |a Mathematisches Modell | |
650 | 4 | |a Biochemistry |x Mathematical models | |
650 | 4 | |a Computer Simulation | |
650 | 4 | |a Gene Expression Regulation | |
650 | 4 | |a Gene Regulatory Networks | |
650 | 4 | |a Genomics | |
650 | 4 | |a Molecular Biology | |
650 | 4 | |a Molecular biology |x Mathematical models | |
856 | 4 | 2 | |m HEBIS Datenaustausch |q application/pdf |u 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 |3 Inhaltsverzeichnis |
999 | |a oai:aleph.bib-bvb.de:BVB01-018615142 |
Datensatz im Suchindex
_version_ | 1804140675695902720 |
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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
|
any_adam_object | 1 |
author | Bolouri, Hamid |
author_facet | Bolouri, Hamid |
author_role | aut |
author_sort | Bolouri, Hamid |
author_variant | h b hb |
building | Verbundindex |
bvnumber | BV035755161 |
callnumber-first | Q - Science |
callnumber-label | QH506 |
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callnumber-search | QH506 |
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callnumber-subject | QH - Natural History and Biology |
classification_rvk | WG 1900 |
ctrlnum | (OCoLC)228372433 (DE-599)BVBBV035755161 |
dewey-full | 572.865 |
dewey-hundreds | 500 - Natural sciences and mathematics |
dewey-ones | 572 - Biochemistry |
dewey-raw | 572.865 |
dewey-search | 572.865 |
dewey-sort | 3572.865 |
dewey-tens | 570 - Biology |
discipline | Biologie |
format | Book |
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id | DE-604.BV035755161 |
illustrated | Illustrated |
indexdate | 2024-07-09T22:03:45Z |
institution | BVB |
isbn | 9781848162204 9781848162211 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018615142 |
oclc_num | 228372433 |
open_access_boolean | |
owner | DE-19 DE-BY-UBM DE-11 DE-29T DE-29 |
owner_facet | DE-19 DE-BY-UBM DE-11 DE-29T DE-29 |
physical | XIV, 326 S. Ill., graph. Darst. |
publishDate | 2008 |
publishDateSearch | 2008 |
publishDateSort | 2008 |
publisher | Imperial College Press |
record_format | marc |
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 |
work_keys_str_mv | AT bolourihamid computationalmodelingofgeneregulatorynetworksaprimer |