Systems biology: a textbook
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Weinheim
Wiley-VCH [u.a.]
2009
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis Klappentext |
| Beschreibung: | XXI, 569 S. Ill., graph. Darst. |
| ISBN: | 9783527318742 3527318747 |
Internformat
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Datensatz im Suchindex
| _version_ | 1805092716330090496 |
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CONTENTS PREFACE XVII PART ONE INTRODUCTION TO SYSTEMS BIOLOGY 1 1
INTRODUCTION 3 1.1 BIOLOGY IN TIME AND SPACE 3 1.2 MODELS AND MODELING 4
1.2.1 WHAT IS A MODEL? 5 1.2.2 PURPOSE AND ADEQUATENESS OF MODELS 5
1.2.3 ADVANTAGES OF COMPUTATIONAL MODELING 6 1.3 BASIC NOTIONS FOR
COMPUTATIONAL MODELS 7 1.3.1 MODEL SCOPE 7 1.3.2 MODEL STATEMENTS 8
1.3.3 SYSTEM STATE 8 1.3.4 VARIABLES, PARAMETERS, AND CONSTANTS 8 1.3.5
MODEL BEHAVIOR 9 1.3.6 MODEL CLASSIFICATION 9 1.3.7 STEADY STATES 9
1.3.8 MODEL ASSIGNMENT IS NOT UNIQUE 10 1.4 DATA INTEGRATION 11 1.5
STANDARDS 12 REFERENCES 12 2 MODELING OF BIOCHEMICAL SYSTEMS 13 2.1
KINETIC MODELING OF ENZYMATIC REACTIONS 13 2.1.1 THE LAW OF MASS ACTION
14 2.1.2 REACTION KINETICS AND THERMODYNAMICS 15 2.1.3 MICHAELIS-MENTEN
KINETICS 18 2.1.3.1 HOW TO DERIVE A RATE EQUATION 19 2.1.3.2 PARAMETER
ESTIMATION AND LINEARIZATION OF THE MICHAELIS-MENTEN EQUATION 20 2.1.3.
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/990246027 DIGITALISIERT
DURCH VIII CONTENTS 3.5.2 MODELING OF APOPTOSIS 135 REFERENCES 142 4
MODEL FITTING 147 4.1 DATA FOR SMALL METABOLIC AND SIGNALING SYSTEMS 147
4.1.1 DATABASES FOR KINETIC MODELING 148 4.1.2 MEASURING PROMOTER
ACTIVITIES USING GFP REPORTER GENES 150 4.2 PARAMETER ESTIMATION 152
4.2.1 REGRESSION 153 4.2.2 ESTIMATORS 153 4.2.2.1 METHOD OF LEAST
SQUARES AND MAXIMUM-LIKELIHOOD ESTIMATION 155 4.2.3 IDENTIFIABILITY 155
4.2.4 BOOTSTRAPPING 157 4.2.5 CROSSVALIDATION 158 4.2.6 BAYESIAN
PARAMETER ESTIMATION 159 4.2.7 LOCAL AND GLOBAL OPTIMIZATION 160 4.2.7.1
LOCAL OPTIMIZATION 161 4.2.7.2 GLOBAL OPTIMIZATION 161 4.2.7.3 SAMPLING
METHODS 162 4.2.7.4 GENETIC ALGORITHMS 163 4.3 REDUCTION AND COUPLING OF
MODELS 264 4.3.1 MODEL SIMPLIFICATION 164 4.3.2 TACIT MODEL ASSUMPTIONS
166 4.3.3 REDUCTION OF FAST PROCESSES 167 CONTENTS IX 4.4.9 MODELS ARE
GROWING IN COMPLEXITY 186 REFERENCES 189 5 ANALYSIS OF HIGH-THROUGHPUT
DATA 193 5.1 HIGH-THROUGHPUT EXPERIMENTS 193 5.1.1 DNA ARRAY PLATFORMS 1
93 5.1.2 PLATFORM COMPARISON 196 5.1.3 NEXT GENERATION SEQUENCING 196
5.1.4 IMAGE ANALYSIS AND DATA QUALITY CONTROL 198 5.1.4.1 GRID FINDING
198 5.1.4.2 SPOT QUANTIFICATION 200 5.1.4.3 SIGNAL VALIDITY 200 5.1.5
PREPROCESSING 202 5.1.5.1 GLOBAL MEASURES 203 5.1.5.2 LINEAR MODELS 203
5.1.5.3 NONLINEAR AND SPATIAL EFFECTS 204 5.1.5.4 OTHER APPROACHES 204
5.2 ANALYSIS OF GENE EXPRESSION DATA 205 5.2.1 PLANNING AND DESIGNING
EXPERIMENTS FOR CASE-CONTROL STUDIES 205 5.2.2 TESTS FOR DIFFERENTIAL
EXPRESSION 206 5.2.2.1 DNA ARRAYS 206 5.2.2.2 NEXT GENERATION SEQUENCING
209 5.2.3 MULTIPLE TESTING 209 5.2.4 ROC CURVE ANALYSIS 211 5.2.5
CLUSTERING ALGORITHMS 213 X CONTENTS 6.1.4.3 EXPERIMENTAL IMPLICATIONS -
RNA INTERFERENCE 246 6.2 GENE REGULATION FUNCTIONS 248 6.2.1 THE LAC
OPERON IN ESCHERICHIA COLI 249 6.2.2 GENE REGULATION FUNCTIONS DERIVED
FROM EQUILIBRIUM BINDING 250 6.2.3 OCCUPATION PROBABILITY DERIVED FROM
STATISTICAL THERMODYNAMICS 251 6.2.4 GENE REGULATION FUNCTION OF THE LAC
OPERON 253 6.2.5 TRANSCRIPTIONAL REGULATION IN LARGER NETWORKS 254 6.2.6
NETWORK COMPONENT ANALYSIS 254 6.3 DYNAMIC MODELS OF GENE REGULATION 256
6.3.1 ONE GENE REGULATORY NETWORK: DIFFERENT APPROACHES 256 6.3.2
REPRESENTATION OF A GENE REGULATORY NETWORK AS GRAPH 256 6.3.3 BAYESIAN
NETWORKS 258 6.3.4 BOOLEAN NETWORKS 259 6.3.5 DESCRIPTION WITH ORDINARY
DIFFERENTIAL EQUATIONS 262 6.3.6 GENE EXPRESSION MODELING WITH
STOCHASTIC PROCESSES 264 REFERENCES 267 7 STOCHASTIC SYSTEMS AND
VARIABILITY 271 7.1 STOCHASTIC MODELING OF BIOCHEMICAL REACTIONS 271
7.1.1 CHEMICAL RANDOM PROCESS FOR MOLECULE NUMBERS 272 7.1.2 THE
CHEMICAL MASTER EQUATION 273 7.1. CONTENTS XI 7.3.2.2 APPROXIMATION FOR
NARROW PARAMETER DISTRIBUTIONS 294 7.3.2.3 TEMPORAL PARAMETER
FLUCTUATIONS 295 7.3.3 UNCERTAINTY ANALYSIS OF BIOCHEMICAL MODELS 295
7.3.3.1 SAMPLING OF REACTION ELASTICITIES 297 7.3.4 DISTRIBUTIONS FOR
KINETIC PARAMETERS 298 7.3.4.1 PRINCIPLE OF MINIMAL INFORMATION 298
7.3.4.2 THERMODYNAMIC CONSTRAINTS ON PARAMETERS 299 7.3.4.3 OBTAINING
PARAMETER DISTRIBUTIONS FROM EXPERIMENTAL DATA 299 7.4 ROBUSTNESS 300 7
A.I ROBUSTNESS PROPERTIES IN BIOCHEMICAL SYSTEMS 301 7.4.1.1 BIOLOGICAL
ROBUSTNESS PROPERTIES 301 7A.X.I MATHEMATICAL ROBUSTNESS CRITERIA 301
7.4.1.3 PRECISE ROBUSTNESS IN A BACTERIAL TWO-COMPONENT SYSTEM 301 7.4.2
STRUCTURAL ROBUSTNESS IN LARGE NETWORKS 303 7.4.2.1 BACKUP GENES 303
7.4.2.2 BACKUP PATHWAYS 304 7A3 QUANTITATIVE ROBUSTNESS BY FEEDBACK 304
7A3.1 NEGATIVE FEEDBACK 304 7.4.3.2 INTEGRAL FEEDBACK 306 7.4.4 SCALING
LAWS, INVARIANCE, AND DIMENSIONAL ANALYSIS 306 XII CONTENTS 8.2.3
ADAPTATION MOTIF 329 8.2.4 NEGATIVE FEEDBACK 330 8.2.5 FEED-FORWARD
LOOPS 331 8.2.6 DYNAMIC MODEL OF THE FEED-FORWARD LOOP 332 8.2.7
DYNAMICS AND FUNCTION OF NETWORK MOTIFS 333 8.3 MODULARITY 335 8.3.1
MODULARITY AS A FACT OR AS AN ASSUMPTION 336 8.3.2 ASPECTS OF
MODULARITY: STRUCTURE, FUNCTION, DYNAMICS, REGULATION, AND GENETICS 337
8.3.3 STRUCTURAL MODULES IN CELLULAR NETWORKS 337 8.3.4 MODULAR RESPONSE
ANALYSIS 338 8.3.5 FUNCTIONAL MODULES DETECTED BY EPISTASIS 339 8.3.6
EVOLUTION OF MODULARITY AND COMPLEXITY 341 8.3.6.1 TINKERING AND
ENGINEERING 341 8.3.6.2 ANALOGY IN EVOLUTION 342 8.3.6.3 MODULARITY,
ROBUSTNESS, AND EVOLVABILITY 342 REFERENCES 343 9 OPTIMALITY AND
EVOLUTION 349 9.1 OPTIMALITY AND CONSTRAINT-BASED MODELS 349 9.1.1
OPTIMIZATION BY EVOLUTION 350 9.1.2 OPTIMALITY STUDIES IN SYSTEMS
BIOLOGY 350 9.1.2.1 THE FITNESS FUNCTION 351 CONTENTS XIII 9.3.4
EVOLUTIONARY STABLE STRATEGIES 373 9.3.5 DYNAMICAL BEHAVIOR IN THE
ROCK-SCISSORS-PAPER GAME 374 9.3.6 EVOLUTION OF COOPERATIVE BEHAVIOR 375
9.3.6.1 KIN SELECTION 376 9.3.6.2 OTHER SCENARIOS FOR EVOLUTION OF
COOPERATION 376 9.3.7 YIELD AND EFFICIENCY IN METABOLISM 377 9.3.7.1
TRADE-OFF BETWEEN FAST AND EFFICIENT ENERGY METABOLISM 377 9.3.7.2
MULTICELLULARITY ENABLES CELLS TO PROFIT FROM RESPIRATION 377 REFERENCES
379 10 CELL BIOLOGY 383 10.1 INTRODUCTION 383 10.2 THE ORIGIN OF LIFE
384 10.3 MOLECULAR BIOLOGY OF THE CELL 387 10.3.1 CHEMICAL BONDS AND
FORCES IMPORTANT IN BIOLOGICAL MOLECULES 387 10.3.2 FUNCTIONAL GROUPS IN
BIOLOGICAL MOLECULES 390 10.3.3 MAJOR CLASSES OF BIOLOGICAL MOLECULES
392 10.3.3.1 CARBOHYDRATES 392 10.3.3.2 LIPIDS 392 10.3.3.3 PROTEINS 396
10.3.3.4 NUCLEIC ACIDS 400 10.4 STRUCTURAL CELL BIOLOGY 402 10.4. XIV
CONTENTS 11.5.4 IN SITU HYBRIDIZATION 430 11.6 FURTHER PROTEIN
SEPARATION TECHNIQUES 430 11.6.1 CENTRIFUGATION 430 11.6.2 COLUMN
CHROMATOGRAPHY 431 11.6.3 POLYMERASE CHAIN REACTION 432 11.7 DNA AND
PROTEIN CHIPS 433 11.7.1 DNA CHIPS 433 11.7.2 PROTEIN CHIPS 434 11.8
YEAST TWO-HYBRID SYSTEM 434 11.9 MASS SPECTROMETRY 435 11.10 TRANSGENIC
ANIMALS 436 11.11 RNA INTERFERENCE 437 11.12 CHIP ON CHIP AND CHLP-PET
439 11.13 SURFACE PLASMON RESONANCE 442 11.14 POPULATION HETEROGENEITY
AND SINGLE ENTITY EXPERIMENTS 442 REFERENCES 444 12 MATHEMATICS 449 12.1
LINEAR MODELING 449 12.1.1 LINEAR EQUATIONS 449 12.1.1.1 THE GAUSSIAN
ELIMINATION ALGORITHM 452 12.1.1.2 SYSTEMATIC SOLUTION OF LINEAR SYSTEMS
452 12.1.2 MATRICES 454 12.1.2.1 BASIC NOTIONS 454 12.1.2.2 LINEAR
DEPENDENCY 454 12.1.2.3 BASIC MATRIX OPERATIONS 454 12.1.2.4 DIMENSION
AND RANK 456 CONTENTS XV 13.1.2 TRANSFORMING PROBABILITY DENSITIES 482
13.1.3 PRODUCT EXPERIMENTS AND INDEPENDENCE 482 13.1.4 LIMIT THEOREMS
483 13.2 DESCRIPTIVE STATISTICS 483 13.2.1 STATISTICS FOR SAMPLE
LOCATION 484 13.2.2 STATISTICS FOR SAMPLE VARIABILITY 485 13.2.3 DENSITY
ESTIMATION 486 13.2.4 CORRELATION OF SAMPLES 487 13.3 TESTING
STATISTICAL HYPOTHESES 488 13.3.1 STATISTICAL FRAMEWORK 489 13.3.2 TWO
SAMPLE LOCATION TESTS 492 13.4 LINEAR MODELS 493 13.4.1 ANOVA 493 13.4.2
MULTIPLE LINEAR REGRESSION 495 13.5 PRINCIPAL COMPONENT ANALYSIS 496
REFERENCES 499 14 STOCHASTIC PROCESSES 501 14.1 BASIC NOTIONS FOR RANDOM
PROCESSES 502 14.1.1 REDUCED AND CONDITIONAL DISTRIBUTIONS 503 14.2
MARKOV PROCESSES 505 14.2.1 MARKOV CHAINS 506 14.3 JUMP PROCESSES IN
CONTINUOUS TIME: THE MASTER EQUATION 507 14.4 CONTINUOUS RANDOM
PROCESSES 508 14.4.1 LANGEVIN EQUATIONS 508 14.4.2 THE FOKKER-PLANCK
EQUATION 509 REFERENCES 510 XVI CONTENTS 16.7.1 17 17.1 17.2 17.2.1
17.2.2 17.3 17.4 17.5 CONSENSUSPATHDB 524 REFERENCES 525 MODELING TOOLS
527 INTRODUCTION 527 MATHEMATICA AND MATLAB 528 MATHEMATICA EXAMPLE 530
MATLAB EXAMPLE 531 DIZZY 532 SYSTEMS BIOLOGY WORKBENCH 534 TOOLS
COMPENDIUM 536 REFERENCES 552 INDEX 553
Systems
biology is a highly topical discipline at the intersection of biochemistry,
cell biology, computer science, and systems engineering. This coursebook in
systems biology is tailored to the needs of advanced students of biology, engi¬
neering, and computer science. It has a companion website with solutions
to questions in the book, additional chapters, and computer implementations
of systems biology models. The book is a follow-up of the very successful
Systems Biology in Practice and incorporates the feedback and suggestions of
many lecturers worldwide. The interdisciplinary team of acclaimed authors
have worked closely together to ensure a comprehensive coverage of the topic
in a fluent and compelling style.
Further material is available on
www.wiley-vch.de/home/systemsbiology
Edda Klipp
is a professor for theoretical biophysics at the
Humboldt
University
Berlin. As a member of the Yeast Systems Biology Network, her research interests
include mathematical modeling of biochemical systems, signal transduction,
principles of cellular regulation, and systems biology.
Wolfram Iiebermeister studied physics and holds a PhD in theoretical bio¬
physics from the
Humboldt
University Berlin. In his work on complex biolo¬
gical systems, he concentrates on functional aspects like variability, control
and optimality.
Christoph
Wierling studied biology at the University of
Münster
and obtained a
PhD in biochemistry at the Free University of Berlin. His research interests focus
on modeling and simulation of biological systems and the development of sys¬
tems biology software.
Axel Kowald holds a PhD in mathematical biology from the National Institute
for Medical Research, London. His current research interests focus on systems
biology and the mathematical modeling of processes involved in the biology of
aging.
Hans Lehrach is a director at the Max Planck Institute for Molecular Genetics
in Berlin and was spokesman for the German Human Genome Initiative. His
research interests include functional genomics, technology development and
systems biology.
Ralf Herwig
studied mathematics and physics at the Free University Berlin
and completed a PhD on statistical clustering methods. He has specialized in
integrative bioinformatics
projects covering genomics, proteomics and systems
biology.
ISBN
978-3-527-31874-2
Cover Picture kindly provided by Santiago Ortiz WWW.wiley-vch.cle
9"7ЪЪЬ27
574; |
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| id | DE-604.BV035653990 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T10:16:01Z |
| institution | BVB |
| isbn | 9783527318742 3527318747 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017708521 |
| oclc_num | 427643182 |
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| owner_facet | DE-20 DE-634 DE-526 DE-703 DE-M49 DE-BY-TUM DE-29T DE-29 DE-11 DE-355 DE-BY-UBR DE-578 DE-B768 |
| physical | XXI, 569 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley-VCH [u.a.] |
| record_format | marc |
| spelling | Systems biology a textbook Edda Klipp ... Weinheim Wiley-VCH [u.a.] 2009 XXI, 569 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Systembiologie Biochemical Processes Computer Simulation Genomics Models, Biological Stochastic Processes Systems Biology Systems biology Textbooks Systembiologie (DE-588)4809615-5 gnd rswk-swf Systembiologie (DE-588)4809615-5 s DE-604 Klipp, Edda Sonstige oth text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3152694&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017708521&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017708521&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Systems biology a textbook Systembiologie Biochemical Processes Computer Simulation Genomics Models, Biological Stochastic Processes Systems Biology Systems biology Textbooks Systembiologie (DE-588)4809615-5 gnd |
| subject_GND | (DE-588)4809615-5 |
| title | Systems biology a textbook |
| title_auth | Systems biology a textbook |
| title_exact_search | Systems biology a textbook |
| title_full | Systems biology a textbook Edda Klipp ... |
| title_fullStr | Systems biology a textbook Edda Klipp ... |
| title_full_unstemmed | Systems biology a textbook Edda Klipp ... |
| title_short | Systems biology |
| title_sort | systems biology a textbook |
| title_sub | a textbook |
| topic | Systembiologie Biochemical Processes Computer Simulation Genomics Models, Biological Stochastic Processes Systems Biology Systems biology Textbooks Systembiologie (DE-588)4809615-5 gnd |
| topic_facet | Systembiologie Biochemical Processes Computer Simulation Genomics Models, Biological Stochastic Processes Systems Biology Systems biology Textbooks |
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