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Self-assembly is ubiquitous in different areas of science, for example in crystals and viruses, and also plays crucial roles in nanotechnology. Many commonalities link these self-assembling systems, in spite of their complexity and different length and time scales. In this thesis, we take an interdisciplinary perspective to gain new insights into self-assembly, exploring ways of modelling self-assembling systems that are relevant across these different fields. A challenge in nanotechnology is to develop self-assembling systems capable of generating a desired outcome. An example is graphene nanoribbons, which are a novel type of semiconductor material with great potential in the nanotech industry. In this context, it is unclear which strategies are best for controlling the output of a self-assembly process, either by manipulation of the thermodynamic environment of the assembling system, or other methods of directing self-assembly. We use quantitative modelling of the kinetics of self-assembly as a tool to predict experimental results in self-assembling systems that are too complex for detailed experimental investigation. Self-assembly of viral protein shells is an example from biology. Viruses have evolved niche methods of assembly that are both robust and highly efficient, as the virus mutation rates are very high, especially in RNA viruses. The viruses discussed in this thesis have an added layer of complexity; it is thought that sequence-specific interactions between viral genomes and the protein building blocks of the viral capsids have a strong impact on the assembly process. We have developed here novel analysis techniques for the modelling of this co-assembly scenario. We use these mechanistic insights to develop new theoretical tools to analyse structural data, providing unprecedented insights into the asymmetric organization of the packaged genome. |
