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Title: New computational tools for analysis of biological processes with application to environmental planning
Author: Aloqalaa, Daniyah Ali
ISNI:       0000 0004 7964 2216
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2019
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This thesis studies two biological processes in which we develop and use efficient computer science tools such as conductance, maximum flow, optimisation and apply them to the two biological processes. More specifically, the project aims to introduce novel ways to model and analysis a biological process, called invasion process, as well as examine and develop algorithms for solving problems related to the process. The invasion process is a biological process in which many species are invading their ranges to new ranges, in response to global climate change and land use change. We first propose a suitable way to model and estimate the duration of the invasion process in real heterogeneous landscapes using graph sparsification approach. Then, we propose an innovative method to estimate the duration of the invasion process in real landscapes using network flow theory. Furthermore, we propose a new algorithmic method of changing landscapes by combining network flow methodology with optimisation technique, in order to improve the invasion process. Following that, we compare the proposed methods/algorithms with other known approaches and evaluate them using existing real heterogeneous landscapes. The project also aims to study and understand another biological aspect, which is the standard genetic code (SGC). The SGC is the set of rules by which information encoded in genetic material (DNA or RNA sequences) is translated into proteins (amino acid sequences) by living cells. It was proposed that the structure of the SGC evolved to minimise harmful consequences of mutations and transnational errors. To study this problem, we first introduce a new general methodology to describe the general structure of the SGC. This methodology comes from graph theory and allows us to consider multiple factors in the graph, such as mutations types - transition and transversion. Then, we develop a clustering algorithm to find optimal codes according to the conductance; the optimal code is the code that has a robust structure against mutations of different types. Finally, we compare the structure of the resultant optimal codes from implementing the developed clustering algorithm with the structure of the SGC.
Supervisor: Kowalski, Dariusz ; Wong, Prudence Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral