IEEE-Madison Section September Meeting

Share

This event is $10 for non-members and $5 for members. Everyone is welcome. Pizza buffet lunch will be provided.

Topic: Hidden Markov Models and Graph Theory for Metabolic Modeling. Abstract: The last decade has seen impressive advances in biology, driven by the acquisition of petabytes of genomic information. Among the many long-standing mysteries that have been solved through the application of mathematical theory and engineering tools to biological information systems has been “the great plate anomaly:” many bacteria can be seen under a microscope but not grown under standard conditions. The application of hidden markov models and graph theory to genomic data solved this century-old puzzle. Genes can be found and annotated for enzymatic function using hidden markov models. Metabolic models of the network of enzymatic functions in the genome can then be represented by a graph where edges represent enzymes and the nodes represent products. A case study will show how a globally important “unculturable” marine bacterium known as SAR11 was brought into culture, shedding light on “the great plate anomaly.” It will conclude with a discussion of extensions of this method from single organisms to entire communities of interacting organisms in any environment, from the human body to underwater volcanoes

  Date and Time

  Location

  Contact

  Registration



  • 5445 E Cheryl Pkwy
  • Fitchburg, Wisconsin
  • United States 53711
  • Building: Promega BioPharmaceutical Technology Center
  • Room Number: 122
  • Click here for Map

Staticmap?size=250x200&sensor=false&zoom=14&markers=43.004062%2c 89
  • Co-sponsored by EMB18 Engineering in Medicine and Biology Society
  • Starts 30 July 2013 10:00 AM
  • Ends 17 September 2014 12:00 PM
  • All times are US/Central
  • No Admission Charge
  • Register


  Speakers

James Tripp

James Tripp of DOE Joint Genome Institute

Topic:

Hidden Markov Models and Graph Theory for Metabolic Modeling

The last decade has seen impressive advances in biology, driven by the acquisition of petabytes of genomic information. Among the many long-standing mysteries that have been solved through the application of mathematical theory and engineering tools to biological information systems has been “the great plate anomaly:” many bacteria can be seen under a microscope but not grown under standard conditions. The application of hidden markov models and graph theory to genomic data solved this century-old puzzle. Genes can be found and annotated for enzymatic function using hidden markov models. Metabolic models of the network of enzymatic functions in the genome can then be represented by a graph where edges represent enzymes and the nodes represent products. A case study will show how a globally important “unculturable” marine bacterium known as SAR11 was brought into culture, shedding light on “the great plate anomaly.” It will conclude with a discussion of extensions of this method from single organisms to entire communities of interacting organisms in any environment, from the human body to underwater volcanoes.

Biography: Dr. Tripp is currently a bioinformaticist at the Department of Energy’s
Joint Genome Institute in Walnut Creek, CA. His research interests are microbial
metabolism, microbial ecology, molecular evolution, and improvement of data
quality in public databases housing genomic information. Prior to that, he was the
Bioinformaticist for a joint venture between the University of California at Santa
Cruz and the Monterrey Bay Aquarium Research Institute, with responsibility for
bioinformatics applications running on a 48-cpu cluster. At Oregon State University
he built a prototype genome annotation and data mining system for his own
research, which was then ported to the Web for use by other researchers. He has
shared his research results at Wood’s Hole Oceanographic Institute, Stanford
University, the University of Southern California, the Red Sea Research Center in
Saudi Arabia and the Korean Oceanographic Research and Development Institute.
Prior to his work in genomics and metagenomics, Dr. Tripp was a computer systems
consultant to the health care industry for 20 years. In that capacity he oversaw the
design, development, and implementation of financial systems and data warehouses
for the States of Texas, Virginia, and Connecticut, as well as Blue Cross and Blue
Shield organizations in Washington DC, San Francisco, and Philadelphia. He holds a
Bachelor’s degree in Biology from Cornell University and a PhD in Molecular and
Cellular Biology from Oregon State University.

James Tripp of DOE Joint Genome Institute

Topic:

Hidden Markov Models and Graph Theory for Metabolic Modeling

Biography:

James Tripp of DOE Joint Genome Institute

Topic:

Hidden Markov Models and Graph Theory for Metabolic Modeling

Biography:





Agenda

September IEEE-Madison Section Meeting joint with Engineering in Medicine and Biology Society Madison Chapter. Lunch will be served for $5 fee (IEEE Members) or $10 fee (non-IEEE members)