|
CSE 590C, Sp '15: Reading & Research in Comp. Bio. |
|
 CSE Home |
About Us |
Search |
Contact Info |
| Course Info | CSE 590C is a weekly seminar on Readings and Research in Computational Biology, open to all
graduate students in computational, biological, and mathematical sciences.
| |||||||||||||||||||||||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||
| Theme | Traditionally, we reserve Spring quarter for "homegrown" research --- highlights of work by researchers in the Seattle area. Our Spring schedule is: | |||||||||||||||||||||||||||||||||||||||||||||||||
| Schedule |
| |||||||||||||||||||||||||||||||||||||||||||||||||
| Papers, etc. |
The UW Library is generally a paid subscriber to non-open-access journals we cite. You can freely access these
articles from on-campus computers. For off-campus access, follow the "[offcampus]" links below or look at the
library "proxy server" instructions. You will be
prompted for your UW net ID and password.
| |||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: By considering living systems as information processing systems, we can formulate questions concerning "emergent" systems-level behaviors that include cellular decision making, maintenance of homeostasis and robustness, sensitivity to diverse yet specific types of information in the presence of environmental variability, and coordination of complex macroscopic behavior. I will discuss approaches rooted in algorithmic information theory for relating structure of complex systems to their dynamics. Elements of dynamical systems theory, such as phase transitions, interpreted through the lens of information dynamics can be used to study how living systems optimally bind past discriminations to future actions. We will also consider the information storage capacity embedded in the state space of complex dynamical systems and the conditions under which the system's memory is maximized. These approaches can be used to examine specific biological systems through new biological observables derived from experimental measurement data. I will also describe a framework based on time-frequency representations for analyzing the trade-offs between stability and responsiveness of nonlinear dynamical systems and discuss its application to several models of molecular networks. |
04/27: Dynameomics: From Simulation of All Protein Folds to Amyloidosis to the Design of Amyloid
Inhibitors and Diagnostics -- Valerie Daggett, UW Bioeng
| Abstract: In this seminar I will discuss two long term projects in the lab and how they have recently come together. Our overarching goal is to use realistic computer simulation methods to characterize dynamic processes and ensembles that defy detailed experimental characterization. The first project is our Dynameomics effort, which involves native state and thermal unfolding simulations of representatives of essentially all known protein folds. This endeavor has produced hundreds of terabytes of data and the largest collection of protein structures in the world (5 orders of magnitude larger than the Protein Data Bank) for mining for a variety of uses. The second involves amyloid diseases, which are typically referred to as misfolding diseases but are in fact unfolding diseases followed by misfolding and aggregation. In this endeavor we have simulated the unfolding of dozens of proteins and peptides involved in human amyloid diseases. In comparing these results we "discovered" that they all pass through a novel form of secondary structure, which we dubbed alpha-sheet, and we hypothesize that this structure is linked to toxicity. The two projects have come together, the Dynameomics data have been used to construct libraries for design and combined with our findings from the amyloid project, we are working on the development of therapeutic and diagnostic agents for amyloid diseases. |
05/04: Prioritizing Deleterious Genetic Variation -- Jay Shendure, UW Genome Sci
05/11: Learning the Sequence Determinants of Exon Definition from over 2 Million
Synthetic Splice Variants -- Georg Seelig, UW CSE/EE
|
Authors:
Alexander B. Rosenberg (EE), Rupali P. Patwardhan (GS), Jay Shendure (GS), and Georg Seelig (EE,CSE)
Abstract: Many of the genetic variants in coding regions of human genes cause disease through altered RNA splicing. Measuring the splicing effects of all exonic variants is infeasible, while training predictive models is challenging due to the limited number of variants with experimental data. Here we develop a novel approach that allows us to accurately predict the effects of these variants on splicing. Rather than examining splicing of genomic sequences, we measure splicing patterns of millions of randomized sequences, encompassing 100 million bases of variation. The large size of our dataset allows us to construct a predictive model of splicing as well as gain new mechanistic insights. From these data we learn that multiple sequence motifs regulate exon definition additively rather than cooperatively. We also show that the same motifs regulate exon definition in alternative 5', 3', and cassette exon splicing. Our model of exon definition and model of the human 5' splice site greatly improve prediction of the effects of variants on both alternative 5' and cassette exon splicing. Our results suggest that large scale assays of random or synthetic sequences can also be used to improve our understanding of other complex forms of gene regulation, such as translation or transcription. |
05/18: Efficient alignment of next-gen sequencing reads -- Phil Green, UW Genome Sci
| Abstract: This is a continuation, with more details about methods, of last fall's COMBI talk describing a new read aligner (based on a hybrid hashtable/suffix array strategy) that is ~50 times faster, more accurate, and of comparable memory usage to the widely used BWA-MEM program. I'll try to minimize overlap with the COMBI talk but include enough introductory material to make it comprehensible to anyone who didn't hear it. |
05/25: -- Holiday
06/01: Informational limits, optimal algorithms and a new assembler for RNA-Seq -- Sreeram Kannan, UW EE
CSE's Computational Molecular Biology research group
Interdisciplinary Ph.D. program in Computational Molecular Biology
 |
Computer Science & Engineering University of Washington Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX |
Note on Electronic Access to Journals