(poster for this announcement)

The DIVISION of MATH and NATURAL SCIENCES of REED COLLEGE presents two lectures by

Anne Condon,

 Professor of Computer Science at the University of British Columbia and Associate Dean for Faculty Affairs and Strategic Initiatives.

Anne Condon, by invitation from the Division of Mathematics and Natural Sciences will be giving two lectures at Reed College as part of Reed's Division Speaker series. Anne's research focuses on computational prediction of RNA and DNA structure, with applications to design of novel structures and gene synthesis. She has broader interests in a number of areas of theoretical computer science, and also holds the NSERC/GM Canada Chair for Women in Science and Engineering for the British Columbia region.

For more information contact jimfix@reed.edu.



Divisional Lecture

WEDNESDAY EVENING, April 8th, 7:30 PM
Psychology Auditorium, Room 105

Computational challenges and opportunities in RNA secondary structure prediction and design

DNA and RNA molecules have proven to be very versatile materials. Structures formed by RNA molecules play important regulatory and catalytic roles in the cell, and show promise in therapeutics. Molecular programmers can now design and realize nano-scale structures and sensors, and even simple machines with moving parts, built from DNA.

Function follows form in the molecular world, and so our ability to understand nucleic acid function in the cell, as well as to design novel structures, is enhanced by reliable means for structure prediction. In this talk, we will describe some algorithmic problems that arise in DNA/RNA secondary structure prediction and design and progress in solving these problems, along with background and motivation from both biological and nanoscale engineering contexts.




Mathematics Colloquium Abstract

THURSDAY AFTERNOON, April 9th, 4:10 PM
Eliot Hall, Room 314

Improved algorithms and parameters for RNA secondary structure prediction

Tools for prediction of the secondary structure of nucleic acids are useful, both to analyze naturally-occurring RNA molecules, and to inform the design of novel nucleic acids which are not found in nature. Computational methods for prediction of secondary structure from the base sequence typically rely on a thermodynamic model of structure formation, and aim to predict that structure with minimum free energy (MFE), or the probabilities of base pair formation. A significant challenge is to obtain accurate predictions in a computationally efficient manner.

In this talk, we'll describe two steps forward in meeting this challenge. The first contribution is a new set of thermodynamic parameters, which improve the accuracy of MFE structure prediction. We use optimization and machine learning methods to infer our parameters from a large repository of known structures, as well as from thermodynamic data obtained experimentally. Secondly, we will describe new combinatorial algorithms for MFE pseudoknotted structure prediction, which are more efficient than previous algorithms for certain types of pseudoknotted structures. We will also discuss possible directions for further improvements in secondary structure prediction.