Doctoral candidate Greg Ryslik’s work in the Department of Biostatistics is focused on bioinformatics, a branch that deals with methods for generating useful biological knowledge.
Now in his fourth year of study, he is well into his own research of mapping where somatic mutations occur on a protein while taking into account the protein’s tertiary structure. Recent theory suggests that there are only a few key “driver” mutations responsible for the formation of cancer cells, explains Greg. “The mutations need to be in the right place in order to result in a gain of function, otherwise the protein usually just dies off.” By mapping amino acids to their 3D positions, he can identify clusters of suspect mutations that are potentially indicative of driver mutations and are thus potential targets for future pharmacological treatment.
Greg first discovered this line of inquiry during a three-month fellowship at the Institute for Pure and Applied Math at UCLA. That work, however considered the proteins only as linear objects. “But the proteins are three dimensional,” says Greg who has developed a number of algorithms to advance his project.
Greg already has several algorithms publicly available on Bioconductor that identify these algorithms and recently co-authored a paper, “The mutational landscape of lethal castration-resistant prostate cancer” that was published in Nature in July 2012. He presented his work on mutation clustering at the first Conference of the International Society for NonParametric Statistics last summer in Greece.