Systems Biology: Properties of Reconstructed Networks

Half-title......Page 2
Title......Page 4
Copyright......Page 5
Dedication......Page 6
Contents......Page 8
Preface......Page 10
Biological parts lists......Page 14
Beyond bioinformatics......Page 15
Genetic circuits......Page 16
1.2 The Systems Biology Paradigm......Page 18
Systemic annotation......Page 19
Hierarchical thinking in systems biology......Page 20
Historical roots......Page 21
Purpose......Page 22
Approach......Page 23
1.5 Further Reading......Page 24
2.1 Components vs. Systems......Page 25
Links......Page 27
2.3 Links to Networks......Page 29
2.4 Constraining Allowable Functional States......Page 32
The constraints under which a cell operates......Page 33
Picking candidate states......Page 34
Hierarchical organization in biology......Page 35
2.6 Further Reading......Page 37
PART ONE Reconstruction of Biochemical Networks......Page 40
3.1 Basic Features......Page 42
Hierarchy in function of metabolic networks......Page 43
Defining the reaction list......Page 46
Publicly available sources of sequence data......Page 48
Biochemical data......Page 50
Protein databases......Page 52
Gene–protein–reaction (GPR) associations......Page 53
Meeting demands and measured physiological states......Page 55
Prospective design of experiments......Page 56
3.3 Genome-scale Metabolic Reconstructions......Page 57
3.4 Multiple Genome-scale Networks......Page 58
Putting \"content in context\"......Page 59
Data types accounted for in a multinetwork reconstruction......Page 62
Regulation of metabolic networks......Page 63
Regulation of enzyme activity......Page 64
3.6 Further Reading......Page 65
4.1 Basic Properties......Page 67
The lacoperon in Escherichia coli......Page 68
The GAL regulon in yeast......Page 69
Proteins that bind to DNA......Page 70
Fundamental building blocks......Page 72
Hierarchy in transcriptional regulatory networks......Page 73
The magnitude of the task......Page 74
Three fundamental data types......Page 75
Top-down data types......Page 76
Bottom-up data types......Page 78
4.3 Large-scale Reconstruction Efforts......Page 79
Early development of the sea urchin......Page 80
Regulation of metabolism in E. coli......Page 82
Formal representation of regulatory networks......Page 83
4.5 Further Reading......Page 85
5.1 Basic Properties......Page 87
G-protein signaling......Page 89
The JAK-STAT network......Page 90
Fundamental building blocks......Page 91
Magnitude of the problem......Page 92
Combinatorial features......Page 93
Elements of reconstruction......Page 94
Level of detail in a reconstruction......Page 95
Data sources for reconstruction process......Page 96
Large-scale reconstruction efforts......Page 97
5.4 Further Reading......Page 99
PART TWO Mathematical Representation of Reconstructed Networks......Page 100
6.1 S as a Linear Transformation......Page 102
The four fundamental subspaces......Page 103
The row and null spaces......Page 104
6.2 S as a Connectivity Matrix......Page 105
Reversible conversion......Page 107
A cofactor-coupled reaction......Page 108
6.4 Linear and Nonlinear Maps......Page 109
6.5 The Elemental Matrix......Page 110
Conserved quantities......Page 111
Reaction vectors as connections between these points......Page 112
Metabolic carrier molecules as conserved moieties......Page 114
Protein molecules as conserved moieties......Page 115
The total stoichiometric matrix......Page 116
Example......Page 117
6.7 Summary......Page 118
6.8 Further Reading......Page 119
7.1 The Binary Form of S......Page 120
S is a sparse matrix......Page 121
Connectivities in genome-scale matrices......Page 122
Node connectivity and network states......Page 124
The reaction adjacency matrix Av......Page 125
The reversible reaction......Page 127
Genome-scale matrices......Page 128
7.5 Summary......Page 129
7.6 Further Reading......Page 130
8.1 Dimensions of the Fundamental Subspaces......Page 131
Basis for vector spaces......Page 132
The singular value spectrum......Page 133
Mapping between the singular vectors......Page 135
SVD as a series of transformations......Page 136
Reversible conversion......Page 137
The finite size of the fundamental subspaces......Page 138
Numerical example......Page 139
Bilinear association......Page 140
Linear combinations of fluxes and concentrations......Page 141
8.4 Interpretation of SVD: Systemic Reactions......Page 142
Simple example......Page 145
Decomposition of genome-scale matrices......Page 146
8.5 Summary......Page 147
8.6 Further Reading......Page 148
9.1 Definition......Page 149
Linear basis......Page 150
Nonnegative linear basis......Page 152
Finite or closed spaces......Page 153
Illustrative examples......Page 154
The simple flux split......Page 155
Some key concepts: Mathematics versus biology......Page 156
Perspective: From reactions to pathways......Page 157
The flux cone......Page 158
Classification of the extreme pathways......Page 159
Simple reactions......Page 160
Skeleton metabolic pathways......Page 161
Computing extreme pathways......Page 162
History of convex pathway vectors......Page 163
Contrasting elementary modes and extreme pathways......Page 164
9.5 Further Reading......Page 165
10.1 Definition......Page 167
Pool sizes......Page 168
Classifying the pools......Page 169
Reference states......Page 170
Simple reversible reaction......Page 171
Carrier-coupled reaction......Page 173
Redox carrier coupled reactions......Page 175
10.4 Multiple Reactions and Pool Formation......Page 176
Combining elementary reactions......Page 177
Multiple redox coupled reactions......Page 178
Simplified glycolysis......Page 179
Simplified TCA cycle......Page 180
10.7 Further Reading......Page 181
The reaction vectors form the basis for the column space......Page 183
Simple examples......Page 184
A basis for the row space......Page 186
Thermodynamic driving forces......Page 187
11.4 Further Reading......Page 188
PART THREE Capabilities of Reconstructed Networks......Page 190
Physics......Page 192
Biology......Page 194
Hierarchy......Page 195
Constraining behaviors......Page 196
Successive imposition of constraints......Page 197
Developing genome-scale models......Page 199
A limited analogy to the engineering design process......Page 200
Redundancy, multifunctionality, and noncausality......Page 201
Failure modes......Page 202
In silico models as hypotheses......Page 203
Experimental designs to probe network functions......Page 205
Physicochemical constraints......Page 206
Environmental constraints......Page 207
Mathematical representation of constraints: balances and bounds......Page 208
Illustrative example......Page 209
12.5 Constraint-Based Analysis Methods......Page 210
12.7 Further Reading......Page 212
The pathway matrix......Page 214
Systems properties of interest......Page 215
Example systems......Page 216
13.2 Pathway Length......Page 218
Genome-scale studies......Page 221
13.3 Reaction Participation and Correlated Reaction Subsets......Page 222
Reaction participation in the JAK-STAT signaling network......Page 223
Correlated subsets......Page 224
CoSets in core E. coli metabolism......Page 225
Flux-coupling assessment through optimization......Page 226
The IOFA......Page 227
The IOFA for the core E. coli network......Page 228
Definition......Page 229
Classifying crosstalk......Page 230
Crosstalk in the JAK-STAT signaling network......Page 231
Skeleton representation of the core metabolic pathways......Page 232
Growth on two carbon sources (C1 and C2) and oxygen (O2)......Page 234
13.7 The alpha-Spectrum......Page 236
Defining the alpha-spectrum......Page 237
Computing the alpha-spectrum......Page 238
13.8 Summary......Page 239
13.9 Further Reading......Page 240
A simple flux split......Page 241
14.2 Sampling Low-Dimensional Spaces......Page 243
Elimination of redundant constraints in determining…......Page 244
Uniform random sampling......Page 245
Sampling high-dimensional spaces......Page 246
The red blood cell......Page 247
The mitochondrion in human cardiomyocytes......Page 249
Growth of E. coli......Page 250
Defining the ranges of allowable values......Page 251
Normalized histograms and expected value calculation......Page 252
Bimolecular association......Page 253
Multicomponent cofactor coupled reactions......Page 254
14.5 Summary......Page 255
14.6 Further Reading......Page 256
15.1 Finding \"Best\" Flux Distributions Through Optimization......Page 257
15.2 Objective Functions......Page 258
Some issues......Page 259
How LP works......Page 260
The types of solutions found......Page 261
Assessment of the sensitivity of the optimum solution......Page 263
Determining network properties......Page 264
Extreme pathways and optimal states......Page 268
15.5 Producing Biomass......Page 269
Biomass formation in E. coli......Page 270
Maintenance energy requirements......Page 271
Gene deletions......Page 273
Effects of proton balancing......Page 275
15.6 Summary......Page 276
15.7 Further Reading......Page 277
16.1 Overview of Constraint-Based Methods......Page 278
Optimization methods......Page 280
Alternative equivalent optima......Page 281
Flux variability......Page 282
Finding objective functions......Page 283
Varying oxygen uptake rate in the core E. coli network......Page 284
Phenotypic phase planes: varying two parameters......Page 286
PhPP for the core E. coli model......Page 289
Minimization of metabolic adjustment (MOMA)......Page 290
MOMA analysis of core E. coli......Page 291
Bilevel optimization procedures......Page 292
16.6 Further Reading......Page 294
How does it work?......Page 295
17.3 Expanding the scope......Page 296
17.4 Where does the field need to go?......Page 297
17.5 Closing......Page 299
Roman Symbols......Page 300
Greek Symbols......Page 301
Abbreviations......Page 302
APPENDIX B Escherichia coli Core Metabolic Network......Page 307
Bibliography......Page 312
Index......Page 330