Keynotes

Supercomputing in Biology: Towards Understanding Living Systems in Atomic Detail

Karissa Y. Sanbonmatsu
Los Alamos National Laboratory
9 November 2010

Abstract

Biotechnology has achieved exquisite control over many biological systems. However, to design drugs computationally, we need to understand these systems at the atomic level of detail well enough to predict their behavior. While we have not achieved this goal, advances in high-performance computing are helping considerably. The advent of petaflop-scale computing has opened the door to realistic simulations of biomolecular systems, making it possible to simulate major drug targets, such as the ribosome, in atomic detail. In addition to being the target of approximately half of antibiotics used in US hospitals, the ribosome is also interesting from an evolutionary point of view. Because many regions of the ribosome are identical in every organism studied to date, the ribosome is arguably the oldest molecular fossil, and represents a bridge between the RNA-world and the modern DNA-RNA-protein world. Analogous to the 'CPU of the living cell', the ribosome performs a look-up table operation, converting the 4-letter alphabet of genetic instructions into the 20-letter alphabet of proteins, the cell's workhorse molecules that perform biochemistry and constitute structures of the cell. We have used high performance computing to delve into the inner workings of this molecular factory, applying Newton's equations of motion to every atom in the ribosome and its surrounding environment, iterating for billions of time steps. In addition to revealing mechanistic insight unattainable by experimental studies, these simulations present an excellent system to push the limits of supercomputing - even exa-scale simulations are insufficient to simulate the entire process of protein synthesis.

Biography

Karissa Y. Sanbonmatsu

Karissa Sanbonmatsu is a principal investigator in the Theoretical Biology and Biophysics group within the Theoretical Division of Los Alamos National Laboratory. She has been an investigator at Los Alamos since she finished her post-doctoral work in the Division of Applied Physics of Los Alamos National Laboratory in 1999. Sanbonmatsu previously earned her B.A. in Physics from Columbia University and Ph. D. in Astrophysical, Planetary and Atmospheric Sciences at the University of Colorado in Boulder. Her research interests include non-coding RNA, ribosomes, riboswitches, large-scale simulations of molecular machines, robotics, and iteration between simulation and experiment. She established an RNA biochemistry lab in 2007. Sanbonmatsu was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) and has performed the largest biomolecular complex simulation to date.

Big Data, Global Development, and Complex Social Systems

Nathan Eagle
Santa Fe Institute / Massachusetts Institute of Technology
10 November 2010

Abstract

Petabytes of data about human movements, transactions, and communication patterns are continuously being generated by everyday technologies such as mobile phones and credit cards. In collaboration with the mobile phone, internet, and credit card industries, Eagle and colleagues are aggregating and analyzing behavioral data from over 250 million people from North and South America, Europe, Asia and Africa. Eagle discusses projects arising from these collaborations that involve inferring behavioral dynamics on a broad spectrum of scales from risky behavior in a group of MIT freshman to population-level behavioral signatures, including cholera outbreaks in Rwanda and wealth in the UK. The research group is developing a range of large-scale network analysis and machine learning algorithms that will provide deeper insight into human behavior.

Biography

Nathan Eagle

Nathan Eagle is a Visiting Assistant Professor at MIT, a Research Assistant Professor at Northeastern, and an Omidyar Fellow at the Santa Fe Institute. His research involves engineering computational tools, designed to explore how the petabytes of data generated about human movements, financial transactions, and communication patterns can be used for social good. He holds a BS and two MS degrees from Stanford University; his PhD from the MIT Media Laboratory on Reality Mining was declared one of the '10 technologies most likely to change the way we live' by the MIT Technology Review. His academic work has been featured in Science, Nature and PNAS, as well as in the mainstream press.

Avoiding the Classic Catastrophic Computer Science Failure Mode

Ralph Johnson
University of Illinois at Urbana-Champaign
11 November 2010
2010 ACM SIGSOFT Outstanding Research Award

Abstract

Many computer science research efforts fail. Some of this is inevitable, since research is risky. But sometimes the agenda of a group of researchers fails because there is a part of the problem that everyone agrees is crucial, but nobody works on it. Often this is because there are not enough rewards for working on it; it is hard to publish and/or there is no funding. This is more common than you might think; I call it the classic catastrophic computer science failure mode. Design Patterns could have fallen into this trap, but it did not. Other projects have not been so fortunate. This talk will describe warning signs of the classic catastrophic computer science failure mode and how to avoid it.

Biography

Ralph Johnson

Ralph Johnson is a research associate professor at the University of Illinois at Urbana-Champaign. He was the leader of the group that made the first automated refactoring tool, the Smalltalk Refactoring Browser. One of his current projects is Photran, an IDE and refactoring tool for Fortran. He is one of three people who has gone to every OOPSLA.

Ralph Johnson, Erich Gamma, Richard Helm, and John Vlissides (posthumous) are the recipients of the 2010 ACM SIGSOFT Outstanding Research Award in recognition of their significant and lasting research contributions to the practice of software engineering through the development and promotion of design patterns.