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Yanica Said is originally from Malta. She joined Oxford Brookes in 2018 and the title of her thesis is ‘Identification of Novel Properties of Metabolic Systems Through Null-Space Analysis’.
My degree is a cotutelle PhD programme concerning the mathematical modelling of metabolic systems. It is split equally between Oxford Brookes (supervised by Dr Mark Poolman) and the University of Malta (supervised by Prof. Cristiana Sebu).
I learned of the opportunity during a presentation held by my supervisors at the University of Malta. I gained interest in Systems Biology due to its multidisciplinary nature, which incorporates skills from Applied Mathematics, Computer Science, and Biology.
When I first visited Oxford Brookes, I found the environment and support services provided to very welcoming, and the training offered interesting and extensive. Moreover, being able to live in the beautiful cosmopolitan city of Oxford is an amazing experience.
I obtained a BSc (Hons) in Mathematics and Physics from the University of Malta in 2018. My dissertation consisted of developing a Machine Learning algorithm aimed at planning the fastest set of routes for a fleet of delivery vehicles to traverse in order to deliver goods to customers. Prior to embarking on my PhD, I worked at an accounting firm where I assisted in Data Science and IT Auditing projects.
Cellular metabolism consists of all the chemical reactions performed by a cell in order to live and grow. It can be regarded as a network of reactions connected through common intermediate molecules called metabolites. As one would expect, the large number of reactions and relationships involved within a cell make insights difficult to obtain through eye inspection alone. Hence, mathematically devised algorithms are used to inspect the structure of the system by studying the interactions between the different components.
Throughout the years, many algorithms have been developed to generate such insights. However, many are hindered by high inefficiency when studying big models. This project aims to develop techniques and tools that enable analysis to be carried out in a more efficient manner, thus requiring less computational costs. More specifically, it aims to do this by extracting insights from the basic mathematical structure of the system, which, while resulting in less extensive information, can be carried out very quickly. I aim to study multiple optima instances in linear programming, and the relationships revealed through the left null-space of the stoichiometry matrix.
I love the collaborative aspect of my PhD where, apart from having access to the resources of two universities in two different countries, I am also able to travel and meet researchers from different parts of the world. One of my favourite aspects is the freedom to conduct my own research whilst working in a team and benefiting from the extensive knowledge of my supervisors. I like to stay motivated through being inspired by my hard-working colleagues, and by organising my tasks using daily to-do lists.