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Title: Exploration, exploitation & complexity in biological evolution and self-assembly
Author: Johnston, Iain G.
ISNI:       0000 0004 2709 0456
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2010
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Self-assembly --- by which ordered structures spontaneously emerge from disordered components --- and biological evolution --- by which Charles Darwin's "endless forms most beautiful" have emerged from the simple chemistry of prehistoric Earth --- may both be pictured as search processes on high-dimensional landscapes, defined respectively by the concepts of energy and fitness. The rich dynamics of these search processes will be studied in order to explain biologically observed features of self-assembly and evolution. This study will introduce and analyse the behaviour of a model for a paradigmatic example of self-assembly, the icosahedral virus capsid, a symmetric structure formed from interacting protein subunits. Results for the thermodynamics and kinetics of model virus assembly will be presented, and the model will be extended to include complicating effects such as different subunit types and cellular crowding. A more general formalism for analysing self-assembling systems will then be introduced and used to suggest a well-defined complexity measure of universal applicability to self-assembled structures. The biological evolution of simple self-assembling structures will be studied using genetic algorithms. The suitability of this modelling approach and its dependence on the many parameters involved will be investigated. Several active areas of enquiry in the field of evolution, including the evolution of complexity, the presence and effect of neutral networks, fluctuations in evolutionary time series and the emergence of symmetry will be investigated within this framework. Throughout this study, we will use the picture of "exploration and exploitation": different approaches to performing optimisation on an unknown landscape, essentially corresponding to a random search and a hill-climbing approach respectively . We will show that, both in self-assembly and evolution, finding an optimal combination of these two approaches gives rise to many of the observed features in this study.
Supervisor: Louis, Ard A. Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Theoretical physics ; Biophysics ; Evolutionary dynamics ; self-assembly ; genetic algorithms ; landscapes ; search processes