**Written Statement of Dr. Stanley Burt for the Senate Committee on
Commerce, Science, and transportation Subcommittee on Technology,
Innovation, and Competitiveness to be held July 19, 2006.**

Over the past couple of decades many important milestones in biology
have been obtained. These include completing the genomic sequences from
several mammalian genomes, including human, and a producing draft
sequences for several additional genomes. Also, new technologies
allowing for simultaneous measurement of mRNA expression levels for
thousands of transcripts and application of this method to RNA samples
from tumors and normal tissues have identified many genes whose
expression is influenced by cancer and other disease states. Further
improvements in this technology and other discoveries have led to chip
technologies capable of simultaneously monitoring the entire genome for
person-to-person variations including both single nucleotide
polymorphisms (SNPs) and other larger alterations such as deletions and
duplications. Other data derived from experiments that measure microRNA
expression levels and transcription factor binding studies have also
contributed to the extensions of this technology. Finally, measurements
of protein expression levels using high throughput
mass-spectrophotometry and chip based tissue and antibody arrays have
given biologists the ability to correlate changes in mRNA expression
with changes in protein expression levels that may contribute to the
disease process or at least be markers for these altered physiological
states.

New instrumentation in microscopy has allowed for simultaneous
monitoring of cells responses to drugs or other agents in parallel.
Further, con-focal imaging techniques have allowed for multiple slices
of the same fields to be examined in detail so that a three dimensional
image of a specimen can be reconstructed. Whole-animal imaging is also
being used to study drug distribution throughout an animal's tissue.
Other methodologies allowing for higher levels of protein expression and
purification are being leveraged to allow for more direct biochemical
metrics of an enzymes function to be collected. High throughput binding
technologies can be used to determine affinities of proteins with
cofactors and drugs. Better docking software applications now exist to
screen some of these interactions in-silico. Newer, more sensitive and
reliable methods have been developed out to identify protein-protein
interactions. The biomedical literature has also grown dramatically as
all of these new methods and the data associated with each of them has
increased.

Taken together, these new methods and the need to process and analyze
the data produced by them have resulted an explosion in the need for
high performance computing in biology and medicine. This need requires
both increased capacity, as the sheer volume of data generated is
considerable, and also increased capability. One of the confounding
problems associated with the needs analysis of this problem is that
there does not appear to be any single solution to the problem. Because
of the diversity in the algorithmic requirements for analysis of each of
these data types, no particular computer hardware seems suited for all
of the problems. Thus, some of the problems are embarrassingly parallel,
meaning they are ideally suited to a cluster environment. Good examples
of this type of application would be comparing fragments of one genome
to another, where each computation is entirely independent of the other.
Advances in microarray plating technology now allows for increased spot
density. This translates into a tremendous increase in the amount of
data from a single experiment, at a significantly reduced cost. In
addition, since these experiments only produce useful results when they
are run for many samples (e.g. tumor and normal tissues) a greater
volume of data is produced. This is leading to the need for the
biologist to have access to computers with more memory and higher
processor speeds to allow the data to be analyzed in a reasonable time.
Already, the ABCC has received requests from cancer biologists for help
with genomic analysis of promoters, control regions, miRNAs, better
annotation of the genome and comparison of genomes, understanding of
fragile sites, sites of chromosome translocation, and the relationship
to cancer of segmental duplications (see pg. x). In addition, the new
500K SNP chips are flooding researchers with data that requires big
computers to process, store, and interpret. Cancer biologists want new
methods to look at the data and estimate haplotypes and look for
interaction among many loci. This and all of the abovementioned
challenges require the use of HPC resources.

Another area in which cluster computing can be useful is in biomarker
discovery. Aside from prevention, diagnostic tools to detect cancer at
an early stage are of great benefit to patients. Great efforts are being
made to identify biomarkers from gene or protein expression profiles.
One tool being used to find biomarkers is mass spectroscopy, which can
identify proteins and their fragments based on their size and electrical
charge. In mass spectroscopy experiments thousands of spectral peaks are
produced. These peaks are then used to find biomarkers for proteins.
Because there are so many data points that are trying to be fit to few
markers, this can lead to false results because the problem is over
determined. In order to avoid this mistake, one needs to perform
thousands of calculations to develop a consistent set of models to find
the proper biomarker. The ABCC does this by using methods that converge
on a model that has the same biomarkers in each solution, thereby
guaranteeing a biologically relevant answer. This procedure can benefit
from hundreds of processors, but large memory is not needed since each
calculation is independent of the others. The ABCC has been successful
in finding biomarkers for bladder cancer and colorectal cancer.
Hopefully, these markers, which are derived from urine and human serum,
will translate into efficient, inexpensive screens that can be used for
early detection of these cancers.

Another problem that confronts biological computing and cancer research
in particular is the sheer volume of data that must be collected,
analyzed and compared. Data already exists in older databases in many
places and in different formats. Part of the problem is already being
approached by the NCI through its caBIG (Cancer Bioinformatics Grid)
initiatives of NCICB and it involves identifying and leveraging
information technologies that facilitate data interconnectivity, amongst
other goals. In this regard, the development and enforcement of data
exchange standards through caDSR and caCore are designed to bridge the
gap between a clinicians and a bioinformaticists perspective of a set of
genomic data. In order to analyze and house this data, there needs to be
a computational infrastructure and visualization capabilities.
Furthermore, while distributed databases are convenient for data
maintenance, the National Security Agency has found that having all the
data reside locally, where it can be called into computer memory, is
essential for rapid data scanning. This will require HPC resources with
large memory resources. Also, database consolidation is not enough.
There needs to be development of methods for the construction of a
knowledge base in which non-experts, especially clinicians, can query
data from various sources. This will require a serious research effort
in knowledge base development area, although some manufactures have
obtained preliminary results in this area. In addition, because there
are problems suited for both hardware configurations mentioned above,
the data I/O infrastructure must also be able to be connected to both of
these scenarios. Again, because of bandwidth issues resulting from the
sheer volume of the data, this results in a need for new technologies in
computer architectures.

Another complicating factor in data combination and analysis for
biological research is that while massive storage and bandwidth have
become relatively cheap and abundant, the data can not only be from
different sources but it can represent experiments in different scales,
from years to femtoseconds---time scales that go across orders of
magnitude. This is a problem that is referred to as multiscale modeling,
and it is a profound problem in computational science. Solving this
problem will require a commitment of resources to advanced architecture
development, more efficient algorithms, and clever data reduction.

I will now address some computational bottlenecks for a few areas that
the National Cancer Institute has identified for their roadmap.

**Nanotechnology:**

Nanoparticles typically have dimensions smaller than 100 nanometers,
which are smaller than human cells. Nanometer devices smaller than 50
nanometers can easily enter most cells. Nanoscale devices can interact
with biomolecules on both the cell surface and within the cells. Despite
their small size, nanoscale devices can also hold tens of thousands of
small molecules such as a contrast agent or a multi-component diagnostic
system capable of assaying a cell's metabolic state. This can provide a
mechanism for detecting cancer at its earliest stages. Nanoscale
constructs, such as dendrimers and liposomes, can provide customizable
drug delivery to targeted cancer cells or tissues. This has already been
demonstrated experimentally.

While nanoparticles have great promise, it also has to be demonstrated
that they are not toxic to normal tissue. The ABCC is supporting the
NCI's Nanoparticle Characterization Laboratory through modeling of bulk
properties and calculation of atomistic properties. At the nanoscale,
the physical, chemical, and biological properties of matter differ
fundamentally and often unexpectedly from those of corresponding bulk
material because of the quantum mechanical properties of atomic
interactions which are influenced by material variations on the
nanometer scale. Modeling of bulk properties such as surface charge or
shape is not difficult. The calculation of atomic level quantities is a
huge computational issue, even atomistic calculations on quantum dots
are beyond our current capability, and will require large increases in
HPC.

**DRUG DESIGN**:

Over the years there has been great success in drug design using HPC.
Drug design is usually done against a protein target, such as an enzyme
whose function one wants to inhibit. A great recent example is the
discovery of Gleevec, an inhibitor of protein kinase activity, which
brings about complete and sustained remission in nearly all patients in
the early stages of chronic myeloid leukemia. If the structure of the
protein is known, docking calculations can be performed. This usually
involves docking thousands of molecules into an active site and scoring
the resultant interaction. If the docking is done with rigid molecules,
the calculations are fairly trivial. If, however, flexibility is
allowed, and most proteins and ligands do flex, then the problem becomes
enormously computationally expensive.

If the protein structure is not known, and the protein is not similar to
another one, then one must perform *ab initio* structure determination.
David Baker's group at Illinois took approximately 150 CPU days to
determine the structure of the CASP6 target T0281. Also to do a docking
interaction between two proteins took 15 CPU days. He makes particular
note that his group is limited by computational power. Our group has
studied the enzyme mechanism of many enzymes involved in cancer. For an
enzyme named Ras, which is mutated in over 30% of known cancers, we
modeled 1,622 atoms of the protein by molecular mechanics and only 43
atoms by quantum chemistry. These studies took several years and were
bound by computational power. To calculate reaction surfaces normally
takes several months of time on HPCs. Luthey-Schulten's group at
Illinois did molecular dynamics simulations of Imidazole Glycerol
Phosphate Synthase, an enzyme involved in making DNA and RNA. It took 10
hours, 12 hours, and 40 hours to animate one nanosecond on three cluster
machines (with different processor speeds). It takes many nanoseconds of
simulation to just relax the systems to prepare for further simulations.
It has been estimated that to go from nanoseconds to milliseconds will
require an increase in computer capacity of approximately 1,000,000.
This can only be achieved by the combination of improved hardware and
software.

**INTEGRATIVE BIOLOGY**:

Computer aided design of HIV protease inhibitors remains one of the most
successful stories in modern biology. Although this was a remarkable
achievement, the complexity of a single viral particle pales in
comparison to characterizing the complete catalog of the cell (the
proteome) and the full map of the interactions of the members of the
proteome. For a subset of interactions of the proteome, the immunome,
the combinatorial problem of treating all possible pairs in the immunome
(1,000,000 of them) escapes the capacity of current computers.

**SYNZYMES**:

There is great interest both in academia and industry for the creation
of artificial enzymes that are much smaller but duplicate the enzymatic
activity the large natural ones. Because they are smaller, they can be
tethered to other molecules or nanopartices, such as dendrimers or
liposomes, and delivered to a particular targeted area such as a tumor
cell. The ABCC staff has experience in this area. We modeled a
particular inorganic catalyst know as Mn-salen, which is used
commercially in the chemical industry for epoxidation reactions. After
studying this reaction, we were able to convert this catalyst into one
having biological activity and could act as a free radical scavenger.
This could be useful for traumatic injuries, strokes, or even for
cancer. However, this falls into the same category as enzyme mechanism
studies and the calculations take months and months to perform. Complete
characterization of these reactions took several years running on fast
HPCs.

**Specialized Hardware**:

Being able to take advantage of specialized HPC resources and software
written for those resources can lead to dramatic increases in time to
solution. In one instance, the ABCC staff in a research parternership
with several NCI biologists investigated how to rapidly scan for
microsatellites (tandem repeats). Tandem repeats are groups of DNA
nucleotides ranging from two to sixteen bases that are expanded in
several diseases. For example, in normal people there is a pattern of
DNA nucleotides, CAG that is expanded 10-35 times. In Huntington's this
same pattern is expanded between 36-121 times. In the past finding these
repeats were found in a heuristic and probabilistic manner on
conventional computers.

Using specialized hardware such as bit matrix multiply and pop count,
which had been requested by the NSA to be incorporated machines they
were using in order to perform rapid pattern matching, we, along with
industrial programmers, were able to drastically reduce the time to find
all tandem repeats on chromosomes and the entire human genome. To scan a
chromosome of approximately 150 million bases took 2 seconds. To scan
the entire human genome took 2 minutes. We discovered 47 potential
disease sites, 8 of which could be associated with cancer, and we more
than doubled the known numbers of repeats. We also used this specialized
hardware search for another genomic feature, known as segmental
duplications, which are associated with diseases. This involved finding
clusters of DNA bases approximately tens of thousand bases long that are
separated by approximately 1 million bases from another cluster of bases
that are the complimentary complement of the original DNA base cluster.
When these complimentary clusters find each other during replication
they combine and huge sections of the genome are excised. We could not
have done this without these specialized hardware features.

We have also used FPGAs, which are reprogrammable hardware and support
the custom computing needs that are characteristic of data-intensive
problems. We programmed the FPGA for a powerful sequence alignment
algorithm known as Smith-Waterman. The Smith-Waterman alignment method
is a powerful algorithm for aligning sequences in which there may be
gaps and one is trying to find the "best" alignment. This algorithm is
widely used in the biological community but is particularly
computationally demanding. We obtained speed-ups of over a thousand
fold. However, the difficulty is that the programming of FPGAs is not a
trivial task, and one that would not be normally within the expertise of
a biologist. However, FPGAs offer great promise because there are
expected to be huge increases in performance on these types of machines.

**Recommendations**:

It has been said that biology will be the science of the 21st century.
Due to the complexity of biology, the sheer volume of data, the fact
that the environment of a cell, (particularly for cancerous cells) must
be taken into account means that biology must be tackled using a systems
biology approach. This means that teams of scientists such as
biologists, computer scientists, mathematicians, physicists, and
chemists should work on these problems in conjunction. In order to do
this, it will require cross training to have a meaningful dialogue. I
believe that in order for the United States to remain competitive we
should devote funding to education and training in the above
disciplines. We also need to find mechanisms to encourage young people
to enter the scientific field. I have seen for several years the lack of
U.S. citizens applying for jobs in the ABCC. I believe that this
reflects the national trend.

As biology matures the use of HPC in biological research will grow.
There is clearly a need for large memory assets coupled with fast
processors. I believe that cluster computing will still have its place,
but as the problems grow in size and complexity, the need for HPC
resources will be inevitable. One can already see this trend in Europe
where several national centers have made purchases of Blue Jean
machines, and others have made investments in large memory machines.

There needs to be funding of new computer architectures, specialized
hardware, faster interconnects, etc. One area of funding that is
especially important is software development. One thing holding back HPC
development is that the software available today is not written for HPC
machines. Sometimes software engineers spend considerable time to port
non-parallel applications to parallel machines without much increase in
speed or efficiency. We are running "old" software on newer
architectures. Along with developing new software, research into new
compilers must be encouraged.

I also recommend that the United States fund several centers for
Integrative Computational Technology for Systems Biology. These centers
would provide for the integration of biology with strong computational
infrastructures and analytic tools. These centers need to provide
intuitive, visual interfaces for biologists with real-time interactive
data analysis. These centers could also serve as training facilities and
facilitate communications between scientists of diverse backgrounds,
disciplines, and expertise within a common framework. These centers
would also facilitate the interplay between discovery and
hypothesis-driven science. Several other countries are already creating
such centers.

Maintaining a leadership role is vital for the economic health of the
United States. We need to maintain our leadership in HPC in order to
have the advantage in intellectual property, which is connected to our
economic well-being. Support for our HPC industry is vital. Countries
such as Japan, China, and India are making substantial investments in
HPC. We need to do the same.

The need for supporting HPC extends across all of the hard science
disciplines. I hope that I have been able to show in this statement that
the increased need for this support is arising from biology. A recent
Rand report entitled "The Global Technology Revolution" was prepared for
the National Intelligence Council. In this report it summarizes how the
future will be determined by the intersection of IT and biology, and the
industries such as nanomaterial, materials, and biotechnology that are
spun from this intersection. Clearly, the future is in this area. We
should make the investment now.

Stanley K. Burt, Ph.D.