Natural Sciences Personal Statementfor Oxford & Cambridge

I want to study Natural Sciences because the questions that interest me do not stay neatly inside one subject. I first read about AlphaFold after a biology extension lesson on its performance at CASP14 in 2020. What stayed with me was not the idea that AI had somehow finished protein folding as a subject, but that one problem could sit so naturally across several sciences at once. A protein starts as a sequence of amino acids, which is a chemical structure, yet the way that chain folds determines a biological function. The more I read, the less satisfied I was with treating that as either just chemistry or just biology. I wanted to understand how interactions, energy and modelling fit together, and why a change that looks minor on paper can alter a protein's behaviour completely. That is what drew me to this course. I do not want to separate disciplines too early when the questions that interest me already cross their boundaries. At university, I want to keep working at that boundary between mathematical models, chemical interactions and biological consequences, while becoming more rigorous about what each way of thinking can reveal, and what it cannot.

My A-level subjects have made that interest more precise. In Chemistry, thermodynamics changed the way I thought about folding. I had first imagined it as a simple contest between order and disorder, but learning about entropy, enthalpy and Gibbs free energy made me see that any apparent increase in order in a protein cannot be understood without thinking about the surrounding solvent as well. Kinetics made the problem harder in a useful way: a process can be energetically favourable and still depend on route and rate. In Biology, protein structure and enzyme action showed me why small changes in shape can have large functional consequences. Maths has helped me become more careful with models, rates and the assumptions built into them. Taken together, those subjects have shown me that scientific explanations are powerful partly because they are limited. Each gives a way of seeing, but not the whole picture. That is why I started reading beyond the syllabus. Erwin Schrödinger's What Is Life? interested me because he approached living systems as a physicist asking how order persists at all. Nick Lane's Transformer pushed me in a different direction by arguing for the central importance of metabolism and energy flow. I did not read either book for a neat answer. What stayed with me was that both made me ask what counts as an explanation in the first place. A biological account can describe function clearly, while a chemical or physical account may explain stability or constraint more convincingly. I find that tension productive rather than frustrating.

Outside lessons, I have tried to test that interest in ways that involve problem solving rather than just more reading. Using Isaac Science and working through UKMT Senior Mathematical Challenge papers has made me more patient with quantitative questions that resist an immediate method, especially when a biological problem becomes a mathematical one. Talks from the Royal Institution have also been useful because they present science as argument as well as result. That habit of testing ideas carried into my EPQ, which grew directly from AlphaFold. I asked whether predicting a final protein structure is the same as explaining how folding happens. Alongside reading about AlphaFold and DeepMind's work, I wrote a simple Python model of a chain on a two-dimensional lattice and used NumPy and Matplotlib to compare how changing local interaction rules altered the number of stable conformations produced. The first version was too crude to distinguish meaningfully between several structures, which forced me to think harder about what the model was leaving out instead of just trying to make the graph cleaner. That mattered more than any tidy result. It made me more careful about the difference between a useful abstraction and a misleading one.