I work at the intersection of biology, physics, and computer science, and am broadly interested in big-picture questions related to protein structure and function.
I have a particular interest in protein conformational disorder and self-assembly, with a focus on biological phase separation. Using a combination of numerical simulations and analytical theory, I am working with experimental collaborators to explore and understand (1) the conformational determinants of disordered proteins, (2) how those determinants influence the driving forces for phase separation, and (3) the physical properties of those phase separated states.
I am currently finishing my PhD in Computational Biophysics with Rohit Pappu at Washington University in St. Louis, where I am the Kent & Bonnie Lattig Fellow at the Center for Biological Systems Engineering.
Previously I worked with Michael Linderman at the Icahn Institute of Genomic and Multiscale Biology in New York city, where I developed fast code to analyze massive data-sets in parallel. Before I moved to the United States I did a Masters in Computer Science at Imperial College London, where I worked with Prof. Yike Guo on methods for network integration in systems biology modelling. Prior to that, I did an MBiochem in Biochemistry at the University of Oxford, where I worked with Prof. Mark Sansom to use molecular dynamics simulations to understand membrane proteins, biophysical characterization of IgE with Prof James McDonnell, and biochemical dissection of the cytochrome-C biosynthetic pathway with Prof. James Allen.
My research is focussed on uncovering the general rules that govern how nature uses protein disorder for function, and how those rules are disrupted in disease.
We often think of proteins as precise molecular machines, yet a substantial faction of naturally occuring proteins are partially or entirely disordered, existing in an ensemble of interconverting states. Can we explain and understand the role of structural disorder in function in disease? My thesis work has been split into two distinct but related parts. The first has involved understanding how the primary amino acid sequence of disordered proteins determine their conformational and functional behaviour. The second has involved understanding how the primary sequence determines the collective behaviour of disordered proteins in aggregation and self-assembly.
The amino acid sequences of disordered regions are poorly conserved, yet these regions are abundant and necessary for normal cellular function. I have built on previous work from the lab to develop a general understanding of how a disordered protein's primary amino acid sequence can be used to make general predictions about conformational behaviour. The long term goal of this work is to use sequence information alone to build predictive models to explain mechanism and function. In parallel, ideas developed in the context of disordered proteins have been immensly helpful in better understanding the early stages of protein folding.
Self-assembly and aggregation is often linked to neurodegenerative diseases, as exemplified by the formation of proteinaceous aggregates in Alzheimer's and Parkinson's disease. In recent years the scientific community has discovered that many disordered proteins drive self-assembly to form dynamic protein-rich liquid-droplets. This intracellular liquid-liquid phase separation (LLPS) provides a novel mechanism for cellular organization, leading to the formation of dynamic and complex mixtures of protein and RNA that perform a range of functions, from the cellular stress response to RNA processing. What are the sequence determinants of phase separation, and what are the physical properties of these droplets once they have formed? The answers to these questions have deep implications for the origin of complex behaviour and multicellularity, as well as providing insight into how cells parse and respond to a constant barrage of noisy yet crucial stream of information from their surrounding environment.
I have worked on these topics using a combination of numerical simulations, analytical theory, and statistical analysis. This has required the development of new approaches, theory, and tools to help answer novel questions. It has also involved substantial collaboration with a range of colleagues, and I have been enormously lucky to work with some of the most exceptional scientists around the world.
My previous work involved formal methods for combining distinct signaling networks, the development of high performance and massively parallelized analysis approaches, and understanding how membrane proteins interact with carbon-nanotubes through coarse-grained simulations. I also worked as a corporate due dilligence analyst - a very different but very interesting type of work.
In my copious spare time (...) I manage all of the lab's hardware and software (two independent CPU clusters totalling ~800 cores, and ~100 TBs of storage across three independent RAID arrays), play far too much Ultimate Frisbee, and cook for my constantly hungry wife.
If you have questions or just want to bounce ideas, please feel free to get in contact at email@example.com or on Twitter. I'm always interested to learn new things and am a big fan of collaboration, but if I don't get back to you please just shoot me another email. My CV and references are available on request.