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Michael ThompsonAcademic Title: Professor Phone: 416-978-3575 Office: LM 139 Email: Research Homepage: http://www.chem.utoronto.ca/staff/THOMPSON/ |
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Research in our laboratory is concerned with the selective detection of biomacromolecular recognition interactions at the solid-liquid interface, with particular emphasis on gene probes and proteomics in clinical diagnostics and drug discovery. Current efforts include a multi-faceted approach to signaling mechanisms involving oligonucleotide, DNA/RNA and proteins that are attached to various solid-state devices being employed for biosensor and microarray development. These studies include methods for the immobilization and characterization by X-ray photoelectron spectroscopy, atomic force microscopy, scanning Kelvin microprobe and radiochemical labeling of DNA/RNA and proteins on surfaces. Also, we are examining interfacial transcriptional chemistry and the detection of bacteria such as E.Coli and Clamydia by biosensor-based nucleic acid probe analysis. Of particular interest are the biosensor signaling of HIV-1 mRNA interactions with peptides and drugs, and study of the surface activity of enzymes such as exonucleases and ribozymes. Research conducted by the group is also centered on the design, construction and applications of novel analytical instruments for the study of bio-macromolecules at interfaces. In particular, we are developing scanning Kelvin microprobe (SKM) and magnetic-direct acoustic wave (MARS) equipment. SKM technology is based upon the highly sensitive measurement of Kelvin currents from substrates at sub-micrometer spatial resolution producing tandem images of contact potential difference and topography. Applications include the scanning of nucleic acid microarrays adn characterization of microelectric devices. The MARS system offers very high frequency, sensitive detection of biochemical species at interfaces in a flow-through format. With respect to signals generated by biosensors, the group is addressing the long-standing problem of device calibration in terms of the concentration-response relationship. In this area, neural network techniques are being employed in a self-referent strategy to calibrate sensor signals.
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