The adoption of complex systems thinking to biology has gained momentum over the past decades as scientists have become more aware of the value of considering whole systems rather than individual components. This thesis is an exploration of complexity science tools in the context of modern biology at the cellular level. Three biological systems are introduced and a novel application of complex systems theory applied to each of them. Firstly, a mathematical modelling approach is applied to the problem of understanding how memories are formed through the process of synaptic plasticity. The approach is shown to be able to accurately predict the behaviour of a synapse, allowing it to be used to inform future experiments. Secondly, a novel statistical approach is applied to the problem of predicting the coiled-coil protein structure based on amino acid sequence alone. The implementation and benchmarking of Spiricoil is described, demonstrating that it outperforms the current leading techniques in the field as well as providing comprehensive evolutionary information about coiled coils to the field for the first time. Finally, a network-based predictor is produced with the aim of predicting the correct biological factors required to turn humans cells from one type to another. A system called Mogrify is developed that can integrate gene expression and interaction data in order to produce predictions of reprogramming factors that until now would have only been possible through experimentation. Each of these projects represents a novel contribution to their field and this thesis as a whole provides a model for how complex systems thinking can be used to better understand biological systems. iii
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:654110 |
| Date | January 2015 |
| Creators | Rackham, Owen |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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