Karen Echeverri, PhD

University of Minnesota

Faculty Host: Dr. Ashley Seifert

Sponsored by Biology Ribble Endowment



*Refreshments served at 3:45pm

Dr. Connie Cepko

Harvard

Faculty Host: Dr. Ann Morris

Sponsored by Biology Ribble Endowment



*Refreshments served at 3:45pm

Chris Janetopoulos, PhD

Vanderbilt

Faculty Host: Dr. Vincent Cassone



Sponsored by Biology Ribble Endowment



*Refreshments served at 3:45pm

Hans Thewissen, PhD 

Northeast Ohio Medical University

Faculty Host: Craig Sargent

Sponsored by Department of Biology Ribble Endowment



*Refreshments served at 3:45pm

 

The Gospel of Matthew records a peculiar astronomical event that occurred at the birth of Christ. Could the “Christmas Star” have been a nova, a supernova, a comet, or some other spectacular sight? I will talk about what was visible around the time of the birth of Christ, and describe Kepler's idea that that the Star was a planet alignment that guided the “wise men from the East.”

Title: Informatics and Modeling Platform for Stable Isotope-Resolve Metabolomics



Abstract: Recent advances in stable isotope-resolved metabolomics (SIRM) are enabling orders-of-magnitude increase in the number of observable metabolic traits (a metabolic phenotype) for a given organism or community of organisms.  Analytical experiments that take only a few minutes to perform can detect stable isotope-labeled variants of thousands of metabolites.  Thus, unique metabolic phenotypes may be observable for almost all significant biological states, biological processes, and perturbations.  Currently, the major bottleneck is the lack of data analysis that can properly organize and interpret this mountain of phenotypic data as highly insightful biochemical and biological information for a wide range of biological research applications.  To address this limitation, we are developing bioinformatic, biostatistical, and systems biochemical tools, implemented in an integrated data analysis platform, that will directly model metabolic networks as complex inverse problems that are optimized and verified by experimental metabolomics data.  This integrated data analysis platform will enable a broad application of SIRM from the discovery of specific metabolic phenotypes representing biological states of interest to a mechanism-based understanding of a wide range of biological processes with particular metabolic phenotypes.