I started my academic career studying Mathematics at the University of Warwick completing an MMath, where I focused on the intersection of ergodic theory and stochastic analysis. I then completed a PhD in Biochemistry in Cambridge under the supervision of Prof Kathryn Lilley developing new statistical and machine learning tools for the analysis of mass-spectrometry-based spatial proteomics data. I now work on advanced computational tools for hydrogen-deuterium exchange mass-spectrometry within the department of statistics, and I closely collaborate with the structural and biophysical science group at GSK to decipher the mode of action for small molecules and antibodies. My key interest is developing tools that answer new questions of analytical methods in biochemistry and biophysics with a particular focus on mass-spectrometry. 

Research Interests

Olly is interested in method development for biological and structural mass-spectrometry, NMR and cryo-EM, using tools from Bayesian statistics and machine learning. He is particularly interested in applications of these tools to answer questions in parasite biology, cellular biology, membrane trafficking, the role of post-translational modification on protein function, as well as antibody and small molecule design. He is also interested in the biosecurity implications of advancing biotechnology. 

Selected Publications

• Barylyuk, Konstantin, et al. "A comprehensive subcellular atlas of the Toxoplasma proteome via hyperLOPIT provides spatial context for protein functions." Cell host & microbe 28.5 (2020): 752-766.
• Shin, John JH, et al. "Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers." Nature communications 11.1 (2020): 1-13.
• Crook, Oliver M., et al. "A Bayesian mixture modelling approach for spatial proteomics." PLoS computational biology 14.11 (2018): e1006516.
• Fang, Siqi, et al. "A Bayesian semi-parametric model for thermal proteome profiling." Communications biology 4.1 (2021): 1-15.
• Crook, Oliver M., Lilley, K. S., Gatto, L., & Kirk, P. D. (2019). Semi-supervised non-parametric Bayesian modelling of spatial proteomics. arXiv preprint arXiv:1903.02909.
• Crook, Oliver M., et al. "Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE." bioRxiv (2021).