Click here for class overview, schedule, details, etc.
After that we will meet:
Recitation:
Thursdays 5:00-7:00 PM
LSRC D-wing room D344.
Instructor: Bruce Donald
(www.cs.duke.edu/brd/)
Office hours: M 4-5 PM (meet in N311 and move to LSRC D212), or email me
for another time.
TA: Siyu Wang (Siyu Wang : email -- siyu.wang@duke.edu )
Recitation/office hours:
5-7pm every Thursday, LSRC D-wing room D344.
Class Webpage: www.cs.duke.edu/donaldlab/Teaching/Seminar18/
This seminar course focuses on topics in computational biology. We
will emphasize themes that unite algorithms, modelling, and
experimental results. Topics will include algorithms, modeling, and
experimental validation for several areas, including protein design,
protein:protein interactions, structural biology, structural
immunology, and structure-based drug design.
For those who have taken a class or seminar with me previously, this
semester we will read entirely different papers, so please feel free
to sign up.
Graduate students and undergraduate students are welcome in this
class. In this class I welcome students from diverse backgrounds:
computer science, biochemistry, biology, chemistry, engineering,
physics... is recommended that students be interested in the
connections between computational science and the life sciences as
applied to macromolecules of biological and pharmacological
importance.
In this seminar course students will present both recent and classic
papers from the literature, and also compile notes on these papers.
Class will end in plenty of time for students to attend the SBB
Seminar.
The primary reading for this course will be supplied as papers to the
students. While some of the background for these papers may be
unfamiliar, the class is structured so that students can acquire this
background while preparing to present and discuss the papers.
Specifically, students will read a textbook, that is designed for this
course, in order to prepare for and understand the background to
present the papers. One textbook covers basic algorithms in this area
of computational biology, and their applications. The second covers
recent results in the field of protein design. When the weekly papers
are assigned, relevant chapters of the textbooks will be assigned as
area/background reading. However, student presentations will
concentrate on the papers, not on presenting from the textbooks.
Textbooks:
Overview
| Syllabus
| Schedule
| How to give a good talk
Supplemental Materials
| Some Relevant WWW Links
Course Summary:
"Strictly speaking, molecular biology is not a new
discipline, but rather a new way of looking at organisms
as reservoirs and transmitters of information. This new
vision opened up possibilities of action and intervention
that were revealed during the growth of genetic
engineering."
- Michel Morange,
"A History of Molecular Biology," Harvard University Press.
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. Recent work shows that PGA techniques may be fruitfully applied
to the challenges of structural molecular biology and rational drug
design. Concomitantly, a wealth of interesting computational problems
arise in proposed methods for discovering new pharmaceuticals.