Elsevier S & T

Systems Self-Assembly: Multidisciplinary Snapshots

Krasnogor, Natalio

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Table of contents
  • Cover
  • Series Dedicationv
  • Table of Contentsvii
  • Prefacexi
  • Chapter 1. Self-Organised Nanoparticle Assemblies: A Panoply of Patterns1
  • 1. Introduction1
  • 2. Pattern Formation: Spanning the Nanoscopic to the Macroscopic3
  • 3. Quantifying Morphology and Topology8
  • 4. Evolving to Equilibrium13
  • 5. Conclusions17
  • Acknowledgements18
  • References18
  • Chapter 2. Biomimetic Design of Dynamic Self-Assembling Systems21
  • 1. Introduction21
  • 2. Definitions22
  • 3. Advantages25
  • 4. Biological Examples-Molecular to Macroscopic27
  • 5. Heuristics of Rational DySA Design31
  • 6. DySA Design in Practice: Magnetohydrodynamic Systems33
  • 7. Examples of Bioinspired DySA38
  • 8. Conclusion42
  • References42
  • Chapter 3. Computing by Self-Assembly:DNA Molecules, Polyominoes, Cells49
  • 1. Introduction49
  • 2. Language-Theory Prerequisites51
  • 3. Sticker Systems53
  • 4. Computing with Shapes56
  • 5. Self-Assembly P Systems59
  • 6. Universality of a Restricted Class of Self-Assembly P Systems64
  • 7. One Further Universality Result74
  • 8. Final Remarks76
  • Acknowledgement76
  • References76
  • Chapter 4. Evolutionary Design of a Model of Self-Assembling Chemical Structures79
  • 1. Design of Self-Assembling Chemical Systems79
  • 2. Dynamic-Bonding Dissipative Particle Dynamics (dbDPD)81
  • 3. Genetic Algorithm for Chemical Structures83
  • 4. Results84
  • 5. Discussion96
  • 6. Conclusion97
  • Acknowledgements98
  • References98
  • Chapter 5. Self-Assembly as an Engineering Concept across Size Scales101
  • 1. Introduction101
  • 2. Two-Dimensional Templated Self-Assembly105
  • 3. 2-D Self-Assembly without a Template112
  • 4. Conclusions118
  • References120
  • Chapter 6. Probabilistic Analysis of Self-Assembled Molecular Networks123
  • 1. Introduction123
  • 2. Resource Redundancy Based Fault-Tolerance126
  • 3. Background129
  • 4. Over-all Probabilistic Design Methodology and Framework133
  • 5. Experimental Results and Analysis139
  • 6. Conclusion149
  • References149
  • Chapter 7. The "Programming Language'' of Dynamic Self-Assembly153
  • 1. Introduction153
  • 2. The RAM Computing Model156
  • 3. Proteins as Elements of a RAM Computer158
  • 4. Hierarchical RAM Computing163
  • 5. RAM Programs in Living Systems165
  • 6. Programmed Dynamic Self-Assembly171
  • 7. Conclusion176
  • Acknowledgements178
  • References178
  • Chapter 8. Self-Assembled Computer Architectures181
  • 1. Introduction181
  • 2. Technology for Self-Assembled Computers182
  • 3. Challenges185
  • 4. Architecture Case Studies189
  • 5. Conclusions196
  • References196
  • Chapter 9. Simulation of Self-Assembly Processes Using Abstract Reduction Systems199
  • 1. Introduction199
  • 2. A Short MGS Presentation202
  • 3. Aggregation Processes in MGS204
  • 4. Accretive Growth of Sierpinski Triangles206
  • 5. Handling Arbitrary Shapes210
  • 6. Self-Assembled Polymers214
  • 7. Carving Sierpinski Triangles216
  • 8. Conclusions221
  • Acknowledgements222
  • References223
  • Chapter 10. Computer Aided Search for Optimal Self-Assembly Systems225
  • 1. Introduction225
  • 2. Definitions226
  • 3. Counting to n at tau=2229
  • 4. Finding Optimal Reduced-Size Tile Systems233
  • 5. Conclusions241
  • Acknowledgements243
  • References243
  • Chapter 11. Programmable Self-Assembly-Theoretical Aspects and DNA-Linked Nanoparticles245
  • 1. Introduction245
  • 2. Fundamental Aspects of Programmable Self-Assembly246
  • 3. Sticky Graphs250
  • 4. Programmable Self-Assembly Using DNA-Linked Nanoparticles253
  • 5. Conclusions257
  • References257
  • Chapter 12. From Microscopic Rules to Emergent Cooperativity in Large-Scale Patterns259
  • 1. Introduction259
  • 2. Emergence of Complex Geometries262
  • 3. Dynamic Response of Complex Spin Networks268
  • 4. Conclusions275
  • Acknowledgements278
  • References278
  • Chapter 13. Automated Self-Assembling Programming281
  • 1. Self-Assembly, Self-Organisation and Natural Computation281
  • 2. Evolutionary Algorithms for Parameter and Structural Learning in Self-Assembly Model Systems283
  • 3. Self-Assembly Models for Unconventional Computing296
  • 4. Conclusions303
  • Acknowledgements303
  • References303
  • Index309
  • Colour Plate Section311
Book details
  • Vendor Elsevier S & T
  • SKU 9780444528650
  • ISBN-13 9780080559759
  • Author Krasnogor, Natalio
  • Category Science
  • Subject System Theory

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Self-assembly is a process that creates complex heirarchical structures through the statistical exploration of alternative configurations. These processes occur without external intervention. Self-Assembly processes are ubiquitous in nature. Understanding how nature produces self-assembled systems will represent an enormous leap forward in our technological capabilities. Robustness and versatility are some of the most important properties of self-assembling natural systems.

Although systems where self-assembly occurs, or which are created by a self-assembling process, are remarkably vaired, some common principles are starting to be discerned. The unifying thread throughout the book is the "Computational Nature of Self-Assembling Systems."

*The only book to showcases state-of-the-art self-assembly systems that arise from the computational, biological, chemical, physical and engineering disciplines
*Coherent, integrated view of both book practice examples and new trends with a clearly presented computational flavor
*Written by world experts in each area