Systems biology: constraint-based reconstruction and analysis
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge, United Kingdom
Cambridge University Press
[2015]
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| Subjects: | |
| Online Access: | Klappentext Inhaltsverzeichnis |
| Physical Description: | xviii, 531 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107038851 |
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| 245 | 1 | 0 | |a Systems biology |b constraint-based reconstruction and analysis |c Bernhard O. Palsson, Department of Bioengineering, University of California at San Diego, USA |
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Record in the Search Index
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|---|---|
| adam_text | Systems Biology
Constraint-based Reconstruction and Analysis
Recent technological advances have enabled comprehensive determination of the
molecular composition of living cells. The chemical interactions between many of
these molecules are known, giving rise to genome-scale reconstructed biochemical
reaction networks underlying cellular functions. Mathematical descriptions of the
totality of these chemical interactions lead to genome-scale models that allow the
computation of physiological functions.
Reflecting these recent developments, this textbook explains how such quanti-
tative and computable genotype-phenotype relationships are built using a genome-
wide basis of information about the gene portfolio of a target organism. It describes
how biological knowledge is assembled to reconstruct biochemical reaction networks,
the formulation of computational models of biological functions, and how these mod-
els can be used to address key biological questions and enable predictive biology.
Developed through extensive classroom use, the book is designed to provide stu-
dents with a solid conceptual framework and an invaluable set of modeling tools and
computational approaches.
Detailed lecture slides, along with MATLAB™ and Mathematica™ workbooks,
are available for download at www.cambridge.org/sb.
Bernhard O. Palsson is the Galletti Professor of Bioengineering and Professor of Pedi-
atrics at the University of California, San Diego. For almost 30 years, his research has
focused on the development of large-scale models of biological functions and their
use to solve basic and applied problems in the life sciences. He has authored three
previous textbooks.
Preface xv
List of abbreviations
XVII
1 Introduction 1
1.1 The Genotype—Phenotype Relationship 1
1.2 Some Concepts of Genome-scale Science 3
1.3 The Emergence of Systems Biology 8
1.4 Building Foundations 11
1.5 About This Book 12
1.6 Summary 13
Part I Network Reconstruction 15
2 Network Reconstruction: The Concept 17
2.1 Many Reactions and Their Stoichiometry 17
2.2 Reconstructing a Pathway 18
2.3 Module-by-module Reconstruction 22
2.4 Proteins and Their Many States 25
2.5 Central E. coli Energy Metabolism 29
2.6 Genome-scale Networks 29
2.7 Summary 32
3 Network Reconstruction: The Process 33
3.1 Building Knowledge Bases 33
3.2 Reconstruction is a Four-step Process 35
3.3 Reconstruction is Iterative and Labor-intensive
3.4 The Many Uses of Reconstructions 45
3.5 Summary 49
4 Metabolism in Escherichia coli 50
4.1 Some Basic Facts about E. coli 50
4.2 History 51
4.2.1 Pre-genome era reconstructions 53
4.2.2 Genome era reconstructions 55
4.3 Content of the (J01366 Reconstruction 65
4.4 From a Reconstruction to a Computational Model
4.5 Validation of /J01366 68
4.6 Uses of the E. coli GEM 70
4.7 Summary 73
5 Prokaryotes 75
5.1 State of The Field 75
Vlil
CONTENTS
5.2
5.3
5.4
5.5
5.6
Metabolism in Pathogens 78
Metabolism in Blue-Green Algae 80
Metabolism in Microbial Communities 82
5.4.1 Systems biology of communities 85
5.4.2 Model-based analysis of microbial communities
An Environmentally Important Organism 88
5.5.1 Geobacter sulfurreducens 88
5.5.2 Genome-scale science for Geobacter 90
Summary 95
86
Eukaryotes 96
6.1 Metabolism in Saccharomyces cerevisiae 96
6.1.1 Reconstruction and its uses 96
6.1.2 Community-based reconstruction 98
6.2 Metabolism in Chlamydomonas reinhardtii 101
6.2.1 Metabolic network reconstruction 101
6.2.2 Description of photon usage 102
6.3 Metabolism in Homo sapiens 103
6.3.1 Recon 1 104
6.3.2 Uses of Recon 1 106
6.3.3 Building multi-cell and multi-tissue reconstructions
6.3.4 Mapping Recon 1 onto other mammals 114
6.3.5 Recon 2 114
6.4 Summary 116
7 Biochemical Reaction Networks
117
7.1 Protein Properties 117
7.2 Structural Biology 119
7.3 Transcription and Translation 123
7.4 Integrating Network Reconstructions
7.5 Signaling Networks 131
7.6 Summary 133
128
8 Metastructures of Genomes 134
8.1 The Concept of a Metastructure 134
8.2 Transcriptional Regulatory Networks 138
8.3 Refactoring DNA for Synthetic Biology 142
8.4 The Challenge of Polyomic Data Integration
8.5 Building Mathematical Descriptions 145
8.6 Summary 148
144
Part II Mathematical Properties of Reconstructed Networks
The Stoichiometric Matrix 151
9.1 The Many Attributes of S 151
9.2 Chemistry: S as a Data Matrix
108
149
153
CONTENTS
ix
9.2.1 Elementary biochemical reactions 154
9.2.2 Basic chemistry 155
9.2.3 Example: glycolysis 158
9.3 Network Structure: S as a Connectivity Matrix 161
9.3.1 The maps of S 161
9.3.2 Biological quantities displayed on maps 161
9.3.3 Linearity of maps 165
9.4 Mathematics: S as a Linear Transformation 165
9.4.1 Mapping fluxes onto concentration time derivatives
9.4.2 The four fundamental subspaces 166
9.4.3 Looking into the four fundamental subspaces 167
9.5 Systems Science: S and Network Models 168
9.6 Summary 171
10 Simple Topological Network Properties 172
10.1 The Binary Form of S 172
10.2 Participation and Connectivity 173
10.2.1 Rearranging the stoichiometric matrix 174
10.2.2 Connectivities in genome-scale matrices 175
10.3 Linked Participation and Connectivities 179
10.3.1 The adjacency matrices of S 179
10.3.2 Computation of the adjacency matrices 180
10.4 Summary 182
11 Fundamental Network Properties 184
11.1 Singular Value Decomposition 184
11.1.1 Decomposition into three matrices 184
11.1.2 The content of U, Ճ, and V 185
11.1.3 Key properties of the SVD 188
11.2 SVD and Properties of Reaction Networks 189
11.3 Studying Elementary Reactions using SVD 191
11.3.1 The linear reversible reaction 191
11.3.2 The bi-linear association reaction 193
11.4 Studying Network Structure Using SVD 196
11.5 Drivers and Directions 199
11.5.1 Directions: the column space 199
11.5.2 Drivers: the row space 201
11.5.3 The fundamental subspaces are of a finite size 201
11.6 Summary 202
12 Pathways 204
12.1 Network-based Pathway Definitions 204
12.2 Choice of a Basis 205
12.3 Confining the Steady-state Flux Vector
12.3.1 Finite or closed spaces 208
12.3.2 Importance of constraints 210
165
208
X
CONTENTS
12.4 Pathways as Basis Vectors 212
12.4.1 Some perspective 212
12.4.2 Extreme pathways 214
12.4.3 Classifying extreme pathways 215
12.4.4 The simplest set of linearly independent basis vectors 217
12.4.5 Examples of pathway computation 217
12.5 Summary 220
13 Use of Pathway Vectors 221
13.1 The Matrix of Pathway Vectors 221
13.2 Pathway Length and Flux Maps 222
13.3 Reaction Participation and Correlated Subsets 224
13.4 Input—output Relationships and Crosstalk 228
13.5 Regulation Eliminates Active Pathways 230
13.6 Summary 232
14 Randomized Sampling 233
14.1 The Basics 233
14.2 Sampling Low-dimensional Spaces 234
14.3 Sampling High-dimensional Spaces 237
14.4 Sampling Network States in Human Metabolism 241
14.5 Summary 247
Part III Determining the Phenotypic Potential of Reconstructed
Networks 249
15 Dual Causality 251
15.1 Causation in Physics and Biology 251
15.2 Building Quantitative Models 255
15.2.1 The physical sciences 255
15.2.2 The life sciences 255
15.2.3 Genome-scale models 256
15.3 Constraints in Biology 260
15.4 Summary 263
16 Functional States 264
16.1 Components vs. Systems 264
16.2 Properties of Links 266
16.3 Links to Networks to Biological Functions 267
16.4 Constraining Allowable Functional States 271
16.5 Biological Consequences of Constraints 272
16.6 Summary 276
17 Constraints 277
17.1 Genome-scale Viewpoints 277
17.2 Stating and Imposing Constraints
279
CONTENTS
xi
17.3 Capacity Constraints 282
17.4 Constraints from Chemistry 285
17.4.1 Mass conservation 286
17.4.2 Thermodynamics 287
17.4.3 Fluxomics 288
17.5 Regulatory Constraints 289
17.6 Coupling Constraints 291
17.7 Simultaneous Satisfaction of All Constraints 296
17.8 Summary 297
18 Optimization 298
18.1 Overview of Constraint-based Methods 298
18.2 Finding Functional States 300
18.3 Linear Programming: the basics 302
18.4 Genome-scale Models 306
18.5 Summary 311
19 Determining Capabilities 312
19.1 Optimal Network Performance 312
19.1.1 Co-factors 312
19.1.2 Biosynthetic Precursors 313
19.2 Production of ATP 315
19.2.1 Producing ATP aerobically from glucose 315
19.2.2 Producing ATP anaerobically from glucose 319
19.2.3 Optimal ATP production from other substrates 320
19.3 Production of Redox Potential 320
19.3.1 Aerobic production of NADH from glucose 320
19.3.2 Anaerobic production of NADH 324
19.4 Capabilities of Genome-scale Models 325
19.5 Summary 325
20 Equivalent States 327
20.1 Equivalent Ways to Reach a Network Objective 327
20.2 Flux Variability Analysis 330
20.2.1 The concept 330
20.2.2 Flux variability in the core E. coli model 332
20.2.3 Genome-scale results 335
20.3 Extreme Pathways and Optimal States 336
20.3.1 The concept 336
20.3.2 Extreme pathways in the core E. coli metabolic network
20.3.3 Genome-scale results 337
20.4 Enumerating Alternative Optima 339
20.5 Summary 341
21 Distal Causation 342
21.1 The Objective Function
337
342
xii CONTENTS
21.2 Types of Objective Functions 343
21.3 Producing Biomass 344
21.4 Formulating The Biomass Objective Function 350
21.5 Studying the Objective Function 353
21.6 Objective Functions in Practice 353
21.7 Summary 355
Part IV Basic and Applied Uses 357
22 Environmental Parameters 359
22.1 Varying a Single Parameter 359
22.1.1 Robustness analysis 359
22.1.2 The effects of oxygen on ATP production 359
22.1.3 The effects of oxygen uptake rate on growth rate
22.1.4 Sensitivity with respect to key processes 364
22.1.5 Uses of robustness analysis 366
22.2 Varying Two Parameters 368
22.2.1 Phenotypic phase planes 368
22.2.2 Using the PhPP at a small scale 371
22.2.3 Using the PhPP at the genome-scale 371
22.3 Summary 377
23 Genetic Parameters 378
23.1 Single Gene Knock-outs 378
23.1.1 Concept 378
23.1.2 Core E. coli metabolic network 379
23.1.3 Genome-scale studies of essential genes 383
23.1.4 Studying non-lethal gene KOs 386
23.2 Double Gene Knock-outs 390
23.2.1 Core E. coli metabolic network 391
23.2.2 Genome-scale studies 392
23.3 Gene Dosage and Sequence Variation 395
23.4 Summary 397
24 Analysis of Omic Data 398
24.1 Context for Content 398
24.2 Omics Data-mapping and Network Topology 402
24.3 Omics Data as Constraints 403
24.4 Omics Data and Validation of GEM Predictions 405
24.5 Summary 406
25 Model-Driven Discovery 407
25.1 Models Can Drive Discovery 407
25.2 Predicting Gap-filling Reactions 412
25.3 Predicting Metabolic Gene Functions
25.4 Summary 420
362
416
CONTENTS xiii
26 Adaptive Laboratory Evolution 422
26.1 A New Line of Biological Inquiry 422
26.2 Determining the Genetic Basis 424
26.3 Interpretation of Outcomes 427
26.4 A Specific Example of Nutrient Adaptation 431
26.5 General Uses of ALE 433
26.6 Complex Examples of Adaptive Evolution 433
26.7 Summary 437
27 Model-driven Design 438
27.1 Historical Background 438
27.2 GEMs and Design Algorithms 444
27.3 GEMs and Cell Factory Design 446
27.4 Summary 450
Part V Conceptual Foundations 451
28 Teaching Systems Biology 453
28.1 The Core Paradigm 453
28.2 High-throughput Technologies 455
28.3 Network Reconstruction 455
28.4 Computing Functional States of Networks 458
28.4.1 Conversion to a computational model 458
28.4.2 Topological properties 459
28.4.3 Determining the capabilities of networks 459
28.4.4 Dynamic states 462
28.5 Prospective Experimentation 462
28.6 Building a Curriculum 464
28.7 Summary 466
29 Epilogue 467
29.1 The Brief History of COBRA 467
29.2 Common Misunderstandings 469
29.3 Questions in Biology and in Systems Biology 470
29.4 Why Build Mathematical Models? 472
29.5 What Lies Ahead? 473
References 481
Index 510
|
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| spelling | Palsson, Bernhard O. 1957- Verfasser (DE-588)1070383511 aut Systems biology constraint-based reconstruction and analysis Bernhard O. Palsson, Department of Bioengineering, University of California at San Diego, USA Cambridge, United Kingdom Cambridge University Press [2015] © 2015 xviii, 531 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Systembiologie (DE-588)4809615-5 gnd rswk-swf Systembiologie (DE-588)4809615-5 s DE-604 Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027763491&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027763491&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Palsson, Bernhard O. 1957- Systems biology constraint-based reconstruction and analysis Systembiologie (DE-588)4809615-5 gnd |
| subject_GND | (DE-588)4809615-5 |
| title | Systems biology constraint-based reconstruction and analysis |
| title_auth | Systems biology constraint-based reconstruction and analysis |
| title_exact_search | Systems biology constraint-based reconstruction and analysis |
| title_full | Systems biology constraint-based reconstruction and analysis Bernhard O. Palsson, Department of Bioengineering, University of California at San Diego, USA |
| title_fullStr | Systems biology constraint-based reconstruction and analysis Bernhard O. Palsson, Department of Bioengineering, University of California at San Diego, USA |
| title_full_unstemmed | Systems biology constraint-based reconstruction and analysis Bernhard O. Palsson, Department of Bioengineering, University of California at San Diego, USA |
| title_short | Systems biology |
| title_sort | systems biology constraint based reconstruction and analysis |
| title_sub | constraint-based reconstruction and analysis |
| topic | Systembiologie (DE-588)4809615-5 gnd |
| topic_facet | Systembiologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027763491&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027763491&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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