#### Life Science Research Practice: A Revolution in Progress

#### Jim Myers (Jim.Myers@pnl.gov), Ron Taylor (Ron.Taylor@pnl.gov), Yuri Gorby (Yuri.Gorby@pnl.gov), Debbie Gracio (Debbie.Gracio@pnl.gov), Pacific Northwest National Laboratory

*DRAFT requirements white paper in support of the DOE National
Collaboratory Program - January, 2005 -- please send comments to
Jim.Myers@pnl.gov*

Life Sciences research is undergoing a transformation from being
qualitative and descriptive to being quantitative and predictive. The
availability of high-throughput, data-intensive "omics" technologies,
such as genomics, transcriptomics, proteomics, and metabolomics,
together with advances in computationally intensive simulation and the
availability of community data repositories, is revolutionizing the way
biological research is conducted, creating, for the first time, an
opportunity to address exciting biological questions such as the
following \[1\]:

-   What biochemical pathways control a plant's ability to create
    biomass, or a microbe's ability to produce hydrogen?

-   Can we identify natural populations of microbes that degrade or
    immobilize contaminants such as hydrocarbons or metals?

-   What cellular repair mechanisms are employed by bacteria that live
    in environments with ionizing radiation?

-   What communities of microbes are most effective in taking up excess
    carbon from the atmosphere?

The key challenges in achieving the systems-level understanding
necessary to address such questions are coordination and integration --
of expertise, experimental and computational resources, and myriad types
of data -- from multiple independent groups and multiple locations. Due
to the complexity and scale of life sciences research, collaboration is
required across distributed groups as well as within and across
communities. Life scientists have been at the forefront in developing,
evaluating, and adopting collaborative technologies ranging from
remotely controlled instruments \[2,3\], collaborative portals \[4,5\],
and, most successfully, community databases and computational services
\[6,7\]. More recent projects are exploring the use of cross-scale data
integration \[8\] and the use of semantic and grid technologies to
simplify data integration and workflow \[9-11\].

In the next five years, data-centric collaboration in the life sciences
will expand from its roots in genomics and bioinformatics and become a
standard component of next-generation biological research. As it does
so, collaboration and cyberinfrastructure technologies will need to
scale by orders of magnitude in terms of the number of users and groups
employing them, the amount of data accessible, and in the heterogeneity
and complexity of information being assembled. In essence,
systems-oriented research will aim to produce "big science" results by
integrating the effort of thousands of independent research programs
developing model organisms, instruments, theories, analysis techniques,
and models. Research will be planned by distributed teams targeting
multiple outcomes, encompass data from myriad techniques from
distributed data repositories, invoke analysis services and multi-scale
models hosted at remote compute centers, and be richly documented with
provenance and other metadata to allow reanalysis and reuse by others.

High-throughput experiments and simulations already are generating vast
amounts of complex data. For example, high-end Fourier transform ion
cyclotron resonance (FTICR) mass spectrometers generate 20 GB per
sample, and facilities are already generating 250+ GB per day.
High-throughput proteomics facilities such as those planned as part of
the DOE Genomics:GTL program will be able to analyze hundreds of samples
per day, providing hundreds of petabytes of data per year within the
next decade. These data need to be analyzed and interpreted within
specific research efforts and then documented and shared in order to
create knowledge bases. Similarly, biomolecular simulations that relate
structure and function of biological systems will be generating hundreds
of gigabytes for each simulation run. All this information, and tools to
support meaningful integration, visualization, and comparison of results
from different techniques, need to be shared, annotated, archived, and
made accessible to the general biological community. The scale of these
tasks will require new mechanisms throughout the research lifecycle to,
for example, capture rich data and model descriptions; document data
accuracy, quality, and uncertainty; integrate heterogeneous information
from independent sources; and perform data mining and visualization of
high-dimensional information. These technologies will need to be
assembled into advanced environments that support high-level tasks such
as discovering existing resources, specifying and executing complex
workflows and queries, and documenting research while hiding the complex
mechanics involved. Further, while the requirements to scale these
efforts to support the entire life sciences community will necessity
increased reliance on self-describing data and services, personal
interactions within and between distributed groups will also be
important for planning, discussion, decision making, and
troubleshooting.

The requirements noted here represent significant challenges. However,
the existing knowledge and technology base across data and information
management, distributed computing, collaboratories, and semantic
information processing strongly suggest that the requirements can be
met. Success in developing and deploying next-generation data-centric
collaboration capabilities in the life sciences will be critical to
maximizing the value of biological research and in improving our lives.

[Online References:]{.underline}

1.  Biological questions and data management discussion adapted from
    input provided for the DOE Data Management Workshops 2004
    (<http://www-conf.slac.stanford.edu/dmw2004/>) Report, in
    preparation

2.  EMSL Virtual NMR Facility,
    <http://collaboratory.emsl.pnl.gov/virtual/EMSLVNMRF.html>

3.  Telescience^TM^, A Collaborative Environment for Telemicroscopy^TM^
    and Remote Science, <https://telescience.ucsd.edu/>

4.  BioCoRE - A Biological Collaborative Research Environment,
    <http://www.ks.uiuc.edu/Research/biocore/>

5.  BioSPICE Community Web Site :: Biology in silicio,
    <https://community.biospice.org/>

6.  The RCSB Protein Data Bank, <http://www.rcsb.org/pdb/>

7.  Basic Local Alignment Search Tool,
    <http://www.ncbi.nlm.nih.gov/blast/>

8.  Biomedical Informatics Research network -- Home Page,
    <http://www.nbirn.net/>

9.  MicroArray and Gene Expression,
    <http://www.mged.org/Workgroups/MAGE/mage.html>

10. myGrid, <http://www.mygrid.org.uk/>

11. cancer Biomedical Informatics Grid, <http://cabig.nci.nih.gov/>