Systems Biology

Simulation of Dynamic Network States

By: Bernhard Ã?. Palsson

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Hardcover | 26 May 2011

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332 Pages

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Biophysical models have been used in biology for decades, but they have been limited in scope and size. In this book, Bernhard O. Palsson shows how network reconstructions that are based on genomic and bibliomic data, and take the form of established stoichiometric matrices, can be converted into dynamic models using metabolomic and fluxomic data. The Mass Action Stoichiometric Simulation (MASS) procedure can be used for any cellular process for which data is available and allows a scalable step-by-step approach to the practical construction of network models. Specifically, it can treat integrated processes that need explicit accounting of small molecules and protein, which allows simulation at the molecular level. The material has been class-tested by the author at both the undergraduate and graduate level. All computations in the text are available online in MATLAB (R) and Mathematica (R) workbooks, allowing hands-on practice with the material.

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Non-Fiction Reference, Information & Interdisciplinary Subjects Research & Information Information theory Cybernetics & Systems Theory Science Biology, Life Sciences Life Sciences in General Genetics excluding Medical Mathematics Applied Mathematics Mathematical Modelling

Molecular Biology Computing & I.T.Databases Data Mining

Preface	p. xi
Introduction	p. 1
Biological networks	p. 1
Why build and study models?	p. 5
Characterizing dynamic states	p. 6
Formulating dynamic network models	p. 7
The basic information is in a matrix format	p. 11
Studying dynamic models	p. 13
Summary	p. 16
Basic concepts	p. 17
Properties of dynamic states	p. 17
Primer on rate laws	p. 20
More on aggregate variables	p. 25
Time-scale decomposition	p. 28
Network structure versus dynamics	p. 31
Physico-chemical effects	p. 34
Summary	p. 36
Simulation of Dynamic States
Dynamic simulation: the basic procedure	p. 41
Numerical solutions	p. 41
Graphically displaying the solution	p. 43
Post-processing the solution	p. 49
Demonstration of the simulation procedure	p. 51
Summary	p. 56
Chemical reactions	p. 58
Basic properties of reactions	p. 58
The reversible linear reaction	p. 60
The reversible bilinear reaction	p. 62
Connected reversible linear reactions	p. 66
Connected reversible bilinear reactions	p. 70
Summary	p. 75
Enzyme kinetics	p. 76
Enzyme catalysis	p. 76
Deriving enzymatic rate laws	p. 78
Michaelis-Menten kinetics	p. 80
Hill kinetics for enzyme regulation	p. 85
The symmetry model	p. 90
Scaling dynamic descriptions	p. 94
Summary	p. 96
Open systems	p. 97
Basic concepts	p. 97
Reversible reaction in an open environment	p. 100
Michaelis-Menten kinetics in an open environment	p. 104
Summary	p. 107
Biological Characteristics
Orders of magnitude	p. 111
Cellular composition and ultra-structure	p. 111
Metabolism	p. 116
Macromolecules	p. 124
Cell growth and phenotypic functions	p. 128
Summary	p. 131
Stoichiometric structure	p. 132
Bilinear biochemical reactions	p. 132
Bilinearity leads to a tangle of cycles	p. 134
Trafficking of high-energy phosphate bonds	p. 137
Charging and recovering high-energy bonds	p. 145
Summary	p. 149
Regulation as elementary phenomena	p. 150
Regulation of enzymes	p. 150
Regulatory signals: phenomenology	p. 152
The effects of regulation on dynamic states	p. 153
Local regulation with Hill kinetics	p. 156
Feedback inhibition of pathways	p. 161
Increasing network complexity	p. 165
Summary	p. 169
Metabolism
Glycolysis	p. 173
Glycolysis as a system	p. 173
The stoichiometric matrix	p. 175
Defining the steady state	p. 181
Simulating mass balances: biochemistry	p. 185
Pooling: towards systems biology	p. 189
Ratios: towards physiology	p. 199
Assumptions	p. 202
Summary	p. 203
Coupling pathways	p. 204
The pentose pathway	p. 204
The combined stoichiometric matrix	p. 210
Defining the steady state	p. 214
Simulating the dynamic mass balances	p. 216
Pooling: towards systems biology	p. 218
Ratios: towards physiology	p. 219
Summary	p. 222
Building networks	p. 224
AMP metabolism	p. 224
Network integration	p. 231
Whole-cell models	p. 240
Summary	p. 241
Macromolecules
Hemoglobin	p. 245
Hemoglobin: the carrier of oxygen	p. 245
Describing the states of hemoglobin	p. 248
Integration with glycolysis	p. 253
Summary	p. 257
Regulated enzymes	p. 259
Phosphofructokinase	p. 259
The steady state	p. 265
Integration of PFK with glycolysis	p. 269
Summary	p. 274
Epilogue	p. 275
Building dynamic models in the omics era	p. 275
Going forward	p. 280
Nomenclature	p. 285
Homework problems	p. 288
References	p. 306
Index	p. 314
Table of Contents provided by Ingram. All Rights Reserved.

Systems Biology

Simulation of Dynamic Network States

At a Glance

Hardcover

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