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[PDF] Systems Biology Properties of Reconstructed Networks – Bernhard o. Palsson

[PDF] Systems Biology Properties of Reconstructed Networks – Bernhard o. Palsson[PDF] Systems Biology Properties of Reconstructed Networks – Bernhard o. Palsson

[PDF] Systems Biology Properties of Reconstructed Networks – Bernhard o. Palsson Book Details

Systems Biology: Properties of Reconstructed Networks

Author(s): Bernhard O. Palsson
Publisher: Cambridge University Press, Year: 2006 ISBN: 9780521859035

[PDF] Systems Biology Properties of Reconstructed Networks – Bernhard o. Palsson Table Of Contents

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

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