Coalition of Academic Scientific Computation, September 18, 2002

P I T T S B U R G HP I T T S B U R G HP I T T S B U R G HP I T T S B U R G HP I T T S B U R G H

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C E N T E RC E N T E RC E N T E RC E N T E RC E N T E R

High Performance Computingand

Biomedical Research

Ralph RoskiesProfessor of Physics-University of Pittsburgh

Scientific Director-Pittsburgh Supercomputing Center

Coalition for Academic Scientific Computation, September 18, 2002

Why the recent emphasis on computing in biomedicine?

Economics- in 15 years, for the same cost Can do 10,000 times more processing Can store 100,000 times more data

Software developments New algorithms for processing The Web for finding data

Now possible to do things previously not feasible

Acquiring, storing, finding, accessing large amounts of data (terabytes to petabytes of text, numbers, images)

Assembling large databases and repeatedly reprocessing them to ask new questions

Simulations based on realistic models to understand the data, do predictive, as opposed to descriptive, biology and

medicine

Why High Performance Computing?

HPC is a discovery tool. Bringing more problems within reasonable human

timescales encourages creativity and exploration HPC is a time machine Incorporating more realism in models crosses a

threshold of relevance to experiment

HPC really involves computing, networking, visualization, storage

3.0T MRI Scanner Cray T3E SGI Onyx

Real-time fMRI

In 1996, this needed a supercomputerToday, it’s routine

Compelling biomedical investigations that HPC enables today

Genomics Analyzing and storing images for

revolutionizing medical care Blood flow and heart disease Structural biology

Data ExplosionExponential Growth of GenBank

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Growth in 2002 due to additions up to May 23th!

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courtesy of Thom Dunning,

BioGrid North Carolina

Simulations linking genes and diseases Based on Utah’s resource of 1.5M people,

with their genealogy, record-linked to cancer and death records back to the early 1900s. Typical pedigree goes back 8 generations Need genotypic data on hundreds of people alive

today

University of Utah Division of Genetic Epidemiology and Center for High Performance Computing

Image Analysis USC Microtomography

High-throughput 3D microtomography using data from electron microscopes

Need high performance computing for real-time analysis

Compare 3-D MRI of normal and knockout mice

Allan Johnson-Duke

Blood Flow 1990’s- Realistic geometry Artificial valve design (Charles Peskin et al, Courant Institute;

150 hours C90)

Today- role of turbulence in loosening plaque, leading to embolisms(Henry Tufo et al, Argonne; 104 hours TCS)

Tomorrow- designing heart pumps to minimize damage to individual blood cells

(Jim Antaki et al, University of Pittsburgh, millions of hours TCS?)

Structural Biology e.g.How Do Aquaporins Work?

Aquaporins -proteins which conduct large volumes of water through cell walls while filtering out charged particles like hydrogen ions.

Massive simulation showed that water moves through aquaporin channels in single file. Oxygen leads the way in. Half way through, the water molecule flips over.

That breaks the ‘proton wire’Klaus Schulten et al, U. of Illinois, SCIENCE (April 19, 2002)

35,000 hours TCS

For a given computing capability, certain important problems get solved.

Many problems need more computing power than we currently have

Protein folding Analyzing genomic and proteomic data Cell modeling and metabolism

Protein folding

Critical for drug design, Understanding misfolding

crucial for diseases like Alzheimers and mad cow

Today’s most powerful systems can only simulate microseconds of real time, but folding takes milliseconds or more Villin headpiece

Red-nativeBlue- partially folded

Genomics and Proteomics

Data is increasing rapidly. Computational demands for integrating, mining and analyzing that data grows even faster.

courtesy of Thom Dunning,

BioGrid North Carolina

By 2005Genomic data–petabytesComputational needs- 10

Teraflops

from TimeLogic

Cell modeling

Need to take account of spatial inhomogeneity cell geometry signal variability (stochastic behavior)

Synaptic TransmissionMany

neurological diseases due

to problems of release or

absorption of neurotransmitters like acetylcholine,

glutamate, glycine, GABA,

serotonin

Joel Stiles, PSC and Tom Bartol, Salk- MCell

Unusual Medical Success In Slow Channel Congenital Myasthenic Syndrome,

channel closes slower upon binding. Electrical current continues longer than normal.

Particular patient presented puzzling symptoms Stiles experimented with the model parameters, and

simulations showed that one could explain the symptoms if the receptors also opened slowly- then verified medically

Unusual interplay of simulation and medical diagnosis- depends critically on realistic

geometry and on stochastic modeling

HPC has enormous promise for biomedicine and improving health

See e.g. The Biomedical Information Science and

Technology Initiative (BISTI) report, June 1999 www.nih.gov/about/director/060399.htm

PITAC Report to the President, “Transforming Health Care Through Information Technology” February 2001, www.itrd.gov/pubs/pitac/index.html

Department of Energy, Computational Structural Biology http://cbcg.lbl.gov/ssi-csb/Program.html

PITAC Recommendations for NIH

Pilot projects and Enabling Technology Centers should be established to extend the practical uses of information technology to health care systems and biomedical research NCRR doing some of this, but Resource budgets limited at

$700K. NIBIB?

PITAC recommendations (cont’d)

Programs should be established to increase the pool of biomedical research and health care professionals with training at the intersection of health and information technology. NPEBC programs a start. But biologists will soon be

overtaken by technical developments and the associated analysis needs

PITAC recommendations (cont’d) A scalable national computing infrastructure

should be provided to support the biomedical research community; Still badly needed NSF and civilian DOE have each recently

invested~$100M in HPC infrastructure. Biomedical users are very heavy users of NSF and

DOE facilities. (At PSC, close to 50% this past year). NIH has almost no investment in these or comparable

resources. (PSC has 30 times the compute power of NCI’s ABCC at Frederick)

Hardware is not enough

Also need support people, knowledgeable in both computing and biology to interact with and support the biomedical research community.

Emerging paradigmGrid Computing

Stresses collaboration, seamless access to data wherever located

Multiple ComputersDistributed data sets High speed networksCommon Interface

We urge you to consider:

NIH should establish HPC Centers, with leading-edge hardware, biomedically-oriented support staff, research into relevant algorithms, and vigorous training.

NIH should actively cooperate with NSF, DOE, and other agencies and shoulder their fair share in building the national computing infrastructure.

Comparable budget scale is ~$100M/year .

There are many sites in the nation that could respond credibly to an NIH solicitation for such Center, including many minority institutions as partners

Because computing infrastructure cuts across Institutes, it is not, and will never be the major priority of any Institute

A cross-Institute initiative of this magnitude and importance cannot happen without leadership from the Director