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FEB 20, 2017 8:00 AM PST

Critical Analysis of Biosensor Data Sets - making sure your conclusions and values are believable

Speaker
  • Senior Scientist, Gilead Sciences, Inc
    Biography
      Dr. Papalia (Joe) received his undergraduate degree in Biology from the University of Ottawa, Canada. He pursued biophysical studies for both his Masters and Ph.D. degrees at the University of Toronto. Both theses included work using various biophysical techniques including NMR and CD spectroscopy as well as well as X-ray crystallography. For his Post-Doctoral work, Joe moved to Salt Lake City, Utah to study with Dr. David Myszka, a pioneer in biosensor studies to follow-up on his main interest, the thermodynamics and kinetics of molecular interactions. During his time with Dr. Myszka, Joe worked on various projects including GPCRs as well as helping to coordinate several benchmark studies with participants from around the world. These studies helped establish various standards in the biosensor field. In time he was promoted to Staff Scientist at the University of Utah. Joe was subsequently recruited to the Bay Area by Dr. Scott Klakamp to work at Takeda San Francisco where he added to existing biosensors capabilities at Takeda and additionally, complemented his strength in biosensors with studies using KinExA technology. He was subsequently recruited by Gilead Sciences as a Senior Scientist to fulfil various roles including leading biosensor work in the Department of Biochemistry and serving on various core teams where he provides both biophysical insight and guidance.

    Abstract

    Biosensors are now a well-established technology in the study of small molecules.  This talk will focus on several aspects of this type of work that biosensor users can be expected to explain.  These are: A) Describing Mass Transport: Mass transport is a common phenomenon in biosensor data that can affect the uniqueness of solutions derived from fits.  The origins of mass transport will be described as well as how mass transport has been modeled quantitatively to help explain the lack of uniqueness in solutions.   How mass transport manifests itself with respect to the shape of sensorgrams will also be described. B)  Reproducibility of Biosensor Data: Biosensors are commonly used to derive rate and equilibrium constants for molecular interactions.  Many of these values appear in publications and technical reports.  A commonly asked question about this technology relates to the magnitude of experimental errors that can typically be expected.  Some case examples will be discussed to help orient the audience as to the reliability of values of cited. C) Problematic Data:   Several case examples of actual problematic data that has been collected will be presented.  The dilemma that some of these data sets can pose in their interpretation will be discussed.


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