2015-2017 WRF and Moore/Sloan Postdoctoral Fellow, Statistics
Biological Sciences, Health Sciences, Statistics
Alexander Franks was a Moore/Sloan Data Science and WRF Innovation in Data Science Postdoctoral Fellow from 2015-2017. He is currently an Assistant Professor in Statistics at the University of California, Santa Barbara.
Department of Statistics
Mathias Dorton, Statistics
Daniel Promislow, Pathology
Ph.D. Candidate, Statistics, Harvard University
Sc.M., Applied Mathematics, Brown University, 2010
B.A., Computer Science and Applied Mathematics, Brown University, 2009
Alexander Franks is primarily interested in improving the statistical sophistication of research in systems biology. At UW, he planned to develop a Bayesian statistical methodology for handling large and heterogeneous metabolomic data.
The wealth of large-scale data in biology has made it possible to investigate many new questions about the “metabolome.” The metabolome consists of the set of small molecules involved in the chemical reactions that make organisms function in their environments. Importantly, metabolomic data differs from genomic or proteomic data in scale and scope.
The metabolome consists of as many as 100,000 molecules, of which many metabolites are still unclassified. The molecules also may react in complex ways with exogenous metabolites from the environment. While recent techniques have made it possible to measure tens of thousands of metabolites simultaneously in a variety of conditions, truly understanding the various roles of these molecules requires new statistical techniques for assessing variability in array data, as well as tools for data integration and ways of handling missing data.