[IEEE-bhpjobs] TODAY: Bruce Donald, Algorithmic Challenges in Structural Molecular Biology an

[Walter Heger]

Duke Computer Science Colloquium
D106 Levine Science Research Center, Duke University
11:45 a.m. - 12:45 p.m.
Monday, April 11, 2005

Bruce Donald

Algorithmic Challenges in Structural Molecular Biology
and Proteomics

Abstract

Some of the most challenging and influential opportunities
for Physical Geometric Algorithms (PGA) arise in developing
and applying information technology to understand the
molecular machinery of the cell. Our recent work (and
work by others) shows that many PGA techniques may be
fruitfully applied to the challenges of computational
molecular biology. PGA research may lead to computer
systems and algorithms that are useful in structural
molecular biology, proteomics, and rational drug design.

Concomitantly, a wealth of interesting computational
problems arise in proposed methods for discovering
new pharmaceuticals. In this talk, I'll discuss some
recent results from my lab, including new algorithms
for interpreting X-ray crystallography and NMR (nuclear
magnetic resonance) data, disease classification using mass
spectrometry of human serum, and protein redesign. Our
algorithms have recently been used, respectively, to
reveal the enzymatic architecture of organisms high
on the CDC bioterrorism watch-list, for probabilistic
cancer classification from human peripheral blood, and to
redesign an antibiotic-producing enzyme to bind a novel
substrate. I'll overview these projects, and survey some
of the algorithmic and computational challenges.

Biography

Bruce Donald is the Joan P. and Edward J. Foley Jr
1933 Professor in the Computer Science Department
at Dartmouth. He holds a joint appointment in the
Department of Chemistry and the Department of Biological
Sciences. From 1987-1998, Donald was a professor in the
the Cornell University Computer Science Department, with
a joint appointment in Applied Mathematics. He received
a B.A. from Yale University, and a Ph.D. from MIT. Donald
has worked in research, visiting, and faculty positions at
Harvard, Stanford, Interval Research Corporation, and MIT.

Donald has been a National Science Foundation Presidential
Young Investigator. He has worked in in several research
areas, including Robotics, Microelectromechanical Systems
(MEMS), Computational Biology, Graphics, and Geometric
Algorithms. Donald's latest research interest is in
computational structural biology and drug design. He
was awarded a Guggenheim Fellowship for his work on
algorithms for structural proteomics. Research in the
Donald laboratory is funded by the National Institutes
of Health under the auspices of the National Institute of
General Medical Sciences Protein Structure Initiative.
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Triangle Computer Science Distinguished Lecturer Series
130A North Building, Duke University (telecast from Duke)
4:00pm - 5:00pm
Monday, April 18, 2005

Reception: 3:30pm D344 Levine Science Research Center

Leonidas J. Guibas Stanford University,
Computer Science Department
http://fano.ics.uci.edu/cites/Author/Leonidas-J-Guibas.html

Lightweight Distributed Reasoning in Sensor Networks

Abstract

The distribution of networked sensors in the environment
offers exciting new possibilities for sensing and
monitoring. Sensors can beplaced close to multiple
signal sources so that, collaboratively, they can sense
and reason about wide-area phenomena, while providing
a distributed awareness that no centralized system
can attain. To bring this vision to fruition, however,
several technological challenges remain. One of these
is the design of lightweight distributed algorithms and
protocols that enable the network to self-organize and
then aggregate local information as needed, in order to
reach global conclusions. This must be done in a scalable
and robust way to allow systems with a large and variable
number of relatively frail nodes that are able to deal
with uncertainty and incomplete information. In this talk
we discuss a number of algorithmic paradigms that address
these issues, at least in part. We present examples such as
tracking wide-area phenomena, counting people or vehicles,
and performing identity management.

Biography

Leonidas Guibas obtained his Ph.D. from Stanford in 1976,
under the supervision of Donald Knuth. His main subsequent
employers were Xerox PARC, MIT, and DEC/SRC. He has been at
Stanford since 1984 as Professor of Computer Science. He
has produced several Ph.D. students who are well-known
in computational geometry, such as John Hershberger,
Jack Snoeyink, and Jorge Stolfi, or in computer graphics,
such as David Salesin and Eric Veach. At Stanford he has
developed new courses in algorithms and data structures,
geometric modeling, geometric algorithms, and sensor
networks. Professor Guibas is an ACM Fellow.
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