The book offers new concepts and ideas that broaden reader’s perception of modern science.
Internationally established experts present the inspiring new science of complexity, which discovers new general laws covering wide range of science areas.
The book offers a broader view on complexity based on the expertise of the related areas of chemistry, biochemistry, biology, ecology, and physics.
Contains methodologies for assessing the complexity of systems that can be directly applied to proteomics and genomics, and network analysis in biology, medicine, and ecology.
Author(s): Danail D. Bonchev, Dennis Rouvray
Series: Mathematical and Computational Chemistry
Edition: 1
Publisher: Springer
Year: 2005
Language: English
Pages: 344
Tags: Биологические дисциплины;Матметоды и моделирование в биологии;
CONTENTS......Page 16
1. Introduction......Page 22
2. On the Complexity of the Complexity Concept......Page 24
3. Complexity and Branching......Page 25
4. Complexity of Smaller Molecules......Page 28
5. Augmented Valence as a Complexity Index......Page 37
6. Complexity of Smaller Fullerenes......Page 41
7. Comparison of Local Atomic Environments......Page 46
8. The Role of Symmetry......Page 50
9. Concluding Remarks on the Complexity of Fullerenes......Page 55
10.1. Introductory remarks......Page 57
10.2. Helicity of nanotubes......Page 59
Acknowledgement......Page 64
References......Page 65
1. Introduction: Complex Chemical Systems in Biological Development and Evolution......Page 70
2. Dynamic, Multistability and Cell Differentiation......Page 72
2.1. Cell states and dynamics......Page 74
2.2. Epigenetic multistability: the Keller autoregulatory transcription factor network model......Page 76
2.3. Dependence of differentiation on cell-cell interaction: the Kaneko-Yomo "isologous Ddiversification" model......Page 80
3.1. Oscillatory dynamic oscillations and somitogenesis......Page 86
3.2. The Lewis model of the somitogenesis oscillator......Page 87
4. Reaction-Diffusion Mechanisms and Embryonic Pattern Formation......Page 91
4.2. Axis formation and left-right asymmetry......Page 92
4.3. Meinhardt's models for axis formation and symmetry breaking......Page 93
5. Evolution of Developmental Mechanisms......Page 97
5.1. Segmentation in insects......Page 98
5.2. Chemical dynamics and the evolution of insect segmentation......Page 101
5.3. Evolution of developmental robustness......Page 104
6. Conclusions......Page 110
References......Page 112
1. Introduction: The Nature of the Problem and Why it Has No Clear Solution......Page 117
1.1. The human mind and the external world......Page 119
1.2. Science and the myth of objectivity......Page 120
1.3. Context dependence and self reference......Page 122
2.1. Relational block diagrams......Page 123
2.2. Information as an interrogative. The answer to "why?"......Page 124
2.4. The answer to "why is the whole more than the sum of its parts?"......Page 126
2.5. Reductionism and relational systems theory compared......Page 127
2.7. An example: the [M,R] system and the organism/machine distinction......Page 128
2.9. Newtonian dynamics is not unique; there are alternatives that yield equivalent results......Page 132
2.10. Topology, thermodynamics and relational modeling......Page 134
2.11. The mathematics of science or is all mathematics scientific?......Page 137
3. The Structure of Network Thermodynamics as Formalism......Page 138
3.1. Network thermodynamic modeling is analogous to modeling electric circuits......Page 139
3.3. Characterizing the networks using an abstraction of the network elements......Page 140
3.4. The nature of the analog models that constitute network thermodynamics......Page 141
3.5. The constitutive laws for all physical systems are analogous to the constitutive laws for electrical networks or can be constructed as the models for electronic elements......Page 142
3.6. The resistance as a general systems element......Page 143
3.7. The capacitance as a general systems element......Page 144
3.9. The formal description of a network......Page 146
3.10. The formal solution of a linear resistive network......Page 148
3.12. Linear multiports are based on non-equilibrium thermodynamics......Page 150
4. Simulation of Non-Linear Networks on Spice......Page 153
4.2. Simulation of mass transport in compartamental systems and bulk flow......Page 154
4.4. The canonical representation of linear non-equilibrium systems, the metric structure of thermodynamics, and the energetic analysis of coupled systems......Page 155
4.5. Tellegen's theorem and the onsager reciprocal relations (ORR)......Page 156
5.1. A message from network theory......Page 158
5.2. An "emergent" property of the 2-port current divider......Page 159
5.3. The use of relational systems theory in chemistry and biology: past, present, and future......Page 161
5.4. Conclusion: there is no conclusion......Page 164
References......Page 168
1. Introduction......Page 174
2. Basic Properties of Random Graphs......Page 176
2.1. Degree distribution......Page 177
2.3. Average path length......Page 178
2.4. Clustering......Page 180
2.5. Small-worlds......Page 181
3. Protein Structure and Contact Graphs......Page 183
3.1. Proteins are small worlds......Page 184
3.2. Hierarchical clustering in contact maps......Page 185
4. Protein Interaction Networks......Page 188
4.1. Assortativeness and correlations......Page 190
4.2. Correlation profiles......Page 191
4.3. Proteome model......Page 194
5. Gene Networks......Page 199
6. Overview......Page 206
References......Page 207
1. Some History......Page 209
2.1. Basic notions in graph theory [36-38]......Page 211
2.2. Adjacency matrix and related graph descriptors......Page 213
2.3. Cluster coefficient and extended connectivity......Page 214
2.4. Graph distances......Page 216
2.5. Weighted graphs......Page 219
3.1. Careful with symmetry!......Page 220
3.2. Can Shannon's information content measure topological complexity?......Page 221
3.3. Global, average, and normalized complexity......Page 223
3.4. The subgraph count, SC, and its components......Page 225
3.5. Overall connectivity, OC......Page 228
3.6. The total walk count, TWC......Page 229
4.1. The A/D index......Page 231
4.2. The complexity index B......Page 233
5. Vertex Accessibility and Complexity of Directed Graphs......Page 236
6. Complexity Estimates of Biological and Ecological Networks......Page 239
6.1. Networks of Protein Complexes......Page 240
6.2. Food webs......Page 244
7. Overview......Page 248
References......Page 250
1.1. Introduction......Page 254
1.2. The "what" of modeling and simulation......Page 255
1.3. Back to models......Page 261
1.4. Models in chemistry and molecular biology......Page 263
2.1. Defining complexity: complicated vs. complex......Page 265
2.2. Defining complexity: agents, hierarchy, self-organization, emergence, and dissolvence......Page 267
3.1. Cellular automata......Page 274
3.2. The general structure......Page 275
3.3. Cell movement......Page 279
3.4. Movement (transition) rules......Page 284
3.5. Collection of data......Page 290
4.1. Introduction......Page 291
4.2. Water structure......Page 292
4.3. Cellular automata models of molecular bond interactions......Page 294
4.4. Diffusion in water......Page 297
4.5. Chreode theory of diffusion in water......Page 300
4.6. Modeling biochemical networks......Page 306
5. General Summary......Page 314
References......Page 315
1. Introduction......Page 319
2. Measuring The Effects of Incorporated Constraints......Page 322
3. Ecosystems and Contingency......Page 323
4. Autocatalysis and Non-Mechanical Behavior......Page 327
5. Causality Reconsidered......Page 332
6. Quantifying Constraint in Ecosystems......Page 334
7. New Constraints to Help Focus a New Perspective......Page 340
References......Page 343
B......Page 346
G......Page 347
L......Page 348
O......Page 349
T......Page 350
Z......Page 351
B......Page 352
C......Page 353
E......Page 354
I......Page 355
M......Page 356
P......Page 357
T......Page 358
Z......Page 359
