The laboratory of Gene Expression & Metabolism is seeking a highly motivated doctoral student from a biostatistics, bioinformatics, or computational biology background who is interested research questions at the confluence of gene expression, new measurement technologies (e.g. mass spectrometry-based proteomics), network biology, and supervised machine learning techniques. In particular, the student should be interested in developing methodologies to concurrently analyze transcriptomics and proteomics data measured in paired samples across large populations. In addition to developments in multi-omics and network biology, the project has a clinical aim: how diseases of energy metabolism (e.g. obesity, diabetes) vary across natural populations as a function of interactions between genotype, age, and environment. A strong background in biostatistics and programming is expected to start. The student should be interested in learning standard wetlab techniques in molecular biology in order to do both benchwork and computational work, although the work will be largely computational. Candidates with experience in cell lines, with human data, or with model organisms—particularly mouse and C. elegans—are particularly welcomed, but prior wetlab experience is not prerequisite.
This research project has two main goals. The first is bioinformatics: to perform data-driven analyses to discover hypotheses that improve our understanding of the major regulatory factors driving divergences in complex traits across populations as a function of age, changes in diet, and exercise. The second major goal is to improve concepts and approaches in biostatistics related to the handling and effective use of multi-omics datasets and input data with multiple independent variables. There are two main statistical aims: (1) examine nonlinear associations in the data; and (2) use the multivariate study design to perform causal inference. The major exploratory approaches of this project are focused on computational biology, and the student will aim to observe both general trends in the data, develop new computational tools, and to identify a specific handful of the strongest candidate pathways for validation via in-depth clinical phenotyping, which will be done together with wetlab-focused members of the lab. These goals are intended to improve our understanding of the etiology of metabolic diseases as a function of age and environment, and to push forward more sophisticated methods for taking advantage of the increasingly large, multivariate, and multi-layered datasets that can be generated in physiological samples.
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