Sam Greenbury is a postdoctoral researcher at the EPSRC Centre for Mathematics of Precision Healthcare (CMPH), working with Iain Johnston, Mauricio Barahona, Charles Coombes and collaborators in developing methods for segmenting, inferring and predicting patient pathways in progressive diseases.
He received his PhD in the field of computational evolutionary biology from the University of Cambridge in 2014 under the supervision of Sebastian Ahnert situated within the TCM Group. He previously read Physics at the University of Oxford where he undertook his Master's research in 2009 within the Ard Louis Research Group.
His Master's and PhD theses focussed on general principles and properties that underpin the mapping between genotype and phenotype (the GP map) in systems governed by biological evolution, in particular considering the relationship between sequence and structure in protein complexes, protein folds and RNA secondary structure, as well as the role of topology in genetic regulatory networks.
Subsequent to his PhD, he worked at the Department of Health until 2016, developing and leading analysis on national policy priorities for both social care and maternity services.
et al., 2016, Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability, Plos Computational Biology, Vol:12, Pages:e1004773-e1004773
Greenbury SF, Ahnert SE, 2015, The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype-phenotype maps, Journal of the Royal Society Interface, Vol:12, ISSN:1742-5689
et al., 2014, A tractable genotype-phenotype map modelling the self-assembly of protein quaternary structure, Journal of the Royal Society Interface, Vol:11, ISSN:1742-5689
et al., 2010, The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks, Journal of Theoretical Biology, Vol:267, ISSN:0022-5193, Pages:48-61