Scheme: University Research Fellowship
Organisation: University of Cambridge
Dates: Oct 2014-Sep 2019
Summary: Mathematical representations, or "models" of biological phenomena allow us to test our understanding of different systems. This enables us to describe the system mathematically and explore how the system behaves under realistic conditions. Performing calculations ("simulations") can show how the system may change over time, offering us unique insights into how accurate our assumptions are and making new predictions which can later be tested experimentally. A key limitation however of biological modelling is that the behaviour of natural systems arises from the connection of small scale phenomena (e.g. the atomic structure of proteins) and large scale phenomena (e.g. cellular signalling networks). For example, changes to a protein structure can lead to alterations in whole cell behaviour. Similarly, changes in protein activity may be counter-balanced by feedbacks from downstream partners in the signalling networks.
In my fellowship I am building models of different biological systems which bridge these phenomena. This is difficult typically because of the different types of information each approach requires, so I will be developing approaches based on the engineering concept of a "cyber-physical system". This class of models couple precise, physical models describing one component of the system, with an abstract ("formal") model describing a distinct component or set of components. My research focuses on several exemplar systems which highlight distinct signalling pathways; bacterial nutrient sensing, antibiotic resistance, and cancer development in single cells and tissues. Through the simulation of these models I will gain unique biological insights whilst concurrently exploring the limits of this approach.