University of Cambridge
Public Health and Primary Care

Cells engage intricate methods to respond to stimuli. Proteins, the cellular machinery, chemically modify each other in a communication chain to regulate phenotypes, like an interwoven network of cascading dominos. Traditional biochemical studies have discovered the crucial role of these modifications in tuning protein activities. Due to technical advances, we can now identify hundreds of thousands of these modifications. However, for 95% of even the most well-characterised modification type, the regulators of modification events within signalling networks and the effects that modifications have on cells remain unknown. A fundamental understanding of these intricate patterns is essential before we can learn how they go awry in disease, or how to wire the system for specific outcomes. Biochemical experiments are the gold-standard in determining functional relationships and outcomes, although the time-intensive nature of these experiments provides a challenge. Given combinatorial effects of modifications, even with a fairly modest estimation of 16 modifications per protein with only binary possibilities (modified or not) for the 20,000 human proteins, there would be over one billion modified protein-versions to functionally interrogate. We need innovative, comprehensive methods to rapidly study the regulatory connections and functions of modifications. While broad-scale molecular studies are promising, their reliance on correlations for functional inference have limited the biological insight that can be drawn. I propose to create a new approach, that leverages the expansive scope of systems genetics, and combines it with the exquisitely targeted control of biochemical studies to understand cellular communication. The random assignment of differences in the human genome forms a natural experiment. I will utilise these natural experiments to identify causal regulators of specific modification sites on proteins, pinpoint rate-limiting steps in signalling pathways and clarify the phenotypic roles of specific modification events. Decoding the language that cells use to dynamically regulate outcomes would transform our understanding of biology. Such an advancement would have direct applications in humans, agricultural animals, and plants. It would enable us to find and resolve cellular miscommunications, addressing various diseases, and also pave the way for writing new biological stories through synthetic biotechnology.