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The NIH Bioinformatics and Computational Biology Roadmap


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Title: The NIH Bioinformatics and Computational Biology Roadmap

The NIH Bioinformatics and Computational Biology
  • Peter Lyster PhD
  • Program Director, Computational Biology and
  • National Institute for Biomedical Imaging and
    Bioengineering (NIBIB) at the National Institutes
    of Health (NIH)
  • For the Coalition for Academic Scientific
    Computation (CASC) Winter meeting, February 4,
    2004,Washington DC

User oriented mission statement
  • In ten years, we want every person involved in
    the biomedical enterprise---basic researcher,
    clinical researcher, practitioner, student,
    teacher, policy maker---to have at their
    fingertips through their keyboard instant access
    to all the data sources, analysis tools, modeling
    tools, visualization tools, and interpretative
    materials necessary to do their jobs with no
    inefficiencies in computation or information
    technology being a rate-limiting step.

Computational Biology at the NIHwhy, whence,
what, whither
  • WhyBecause computation and information
    technology is an invaluable tool for
    understanding biological complexity, which is at
    the heart of advance in biomedical knowledge and
    medical practice.
  • You cant translate what you dont
    understand---Elias Zerhouni, Director of the
    National Institutes of Health, commenting on the
    relationship between basic research and
    translational research, that transforms the
    results of basic research into a foundation for
    clinical research and medical practice.

Computational Biology at the NIHwhy, whence,
what, whither
  • WhenceComputation and information technology
    were originally used as add-ons, to add value to
    experimental and observational results that had
    sufficiently simple patterns that they could be
    discerned by observation. Often the computing
    technology was an almost invisible partner to the
    experiments. For example, the 1951
    Hodgkin-Huxley Nobel Prize work that elucidated
    the bases of electrical excitability included
    calculations that were done on an
    electromechanical calculator, and would not have
    been feasible by hand or slide ruleyet it is not
    often cited as an example of the importance of
    calculating technology.

Computational Biology at the NIHwhy, whence,
what, whither
  • WhatToday computation is at the heart of all
    leading edge biomedical science. For leading
    examples, consider this past years Nobel prizes
  • Structure of voltage-gated channelsrequired
    sophisticated computation for image
    reconstruction for x-ray diffraction data, the
    mathematical techniques for which were the
    subject of a previous Nobel prize.
  • Discovery of water channelsThe experimental work
    required augmentation by bioinformatics for
    identification of water channel genes by sequence
  • Magnetic resonance imagingA large share of the
    prize work was for the mathematical and
    computational techniques for inferring structure
    and image from NMR spectra.

Computational Biology at the NIHwhy, whence,
what, whither
  • Institutes and Centers at NIH support substantial
    development and implementation of computation and
    information technology embedded in biomedical
  • Informatics is a key component of the NIH Roadmap

Roadmap Activities Computation
  • New Pathways to Discovery
  • National Centers for Biomedical Computing
  • Building Blocks, Biological Pathways, and
  • Re-engineering the Clinical Research Enterprise
  • National Electronic Clinical Trials and Research
    Network (NECTAR)
  • Dynamic Assessment of Patient-Reported Chronic
    Disease Outcomes
  • Trans NIH Informatics Committee (TNIC)

Present State of Computational Biology Practice
  • Essentially all leading-edge biomedical research
    utilizes significant computing.
  • Development and initial implementation of methods
    are largely the product of collaborations with
    overlapping expertise---biologists who have
    substantial expertise in computing with computer
    scientists and other quantitative scientists who
    have substantial knowledge of biology. Computer
    scientists and other quantitative scientists with
    little knowledge of biology are generally unable
    to contribute to the development of biomedical
    computing tools.

The Paradox of Computational Biology--Its
successes are the flip side of its deficiencies.
  • The success of computational biology is shown by
    the fact that computation has become integral and
    critical to modern biomedical research.
  • Because computation is integral to biomedical
    research, its deficiencies have become
    significant rate limiting factors in the rate of
    progress of biomedical research.

Some important problems with biomedical computing
tools are
  • They are difficult to use.
  • They are fragile.
  • They lack interoperability of different
  • They suffer limitations on dissemination
  • They often work in one program/one function mode
    as opposed to being part of an integrated
    computational environment.
  • There are not sufficient personnel to meet the
    needs for creating better biological computing
    tools and user environments.

Computational Biology at the NIHwhy, whence,
what, whither
  • WhitherThe NIH Bioinformatics and Computational
    Biology Roadmap
  • Was submitted to NIH Director Dr. Elias Zerhouni
    on May 28, 2003
  • Is the outline of an 8-10 year plan to create an
    excellent biomedical computing environment for
    the nation.
  • Has as its explicit most ambitious goal Deploy a
    rigorous biomedical computing environment to
    analyze, model, understand, and predict dynamic
    and complex biomedical systems across scales and
    to integrate data and knowledge at all levels of

1-3 year roadmap goals relatively low difficulty
  • 1. Develop vocabularies, ontologies, and data
    schema for defined domains and develop prototype
    databases based on those vocabularies,
    ontologies, and data schema
  • 2. Require that NIH-supported software
    development be open source.
  • 3. Require that data generated in NIH-supported
    projects be shared in a timely way.
  • 4. Create a high-prestige grant award to
    encourage research in biomedical computing.
  • 5. Provide support for innovative curriculum
    development in biomedical computing
  • 6. Support workshops to test different methods or
    algorithms to analyze the same data or solve the
    same problem.
  • 7. Identify existing best practice/gold standard
    bioinformatics and computational biology products
    and projects that should be sustained and
  • 8. Enhance training opportunities in
    bioinformatics and biomedical computing.

1-3 year roadmap goals moderate difficulty
  • 1. Support Center infrastructure grants that
    include key building blocks of the ultimate
    biomedical computing environment, such as
    integration of data and models across domains,
    scalability, algorithm development and
    enhancement, incorporation of best software
    engineering practices, usability for biology
    researchers and educators, and integration of
    data, simulations, and validation.
  • 2. Develop biomedical computing as a discipline
    at academic institutions.
  • 3. Develop methods by which NIH sets priorities
    and funding options for supporting and
    maintaining databases.
  • 4. Develop a prototype high-throughput global
    search and analysis system that integrates
    genomic and other biomedical databases.

4-7 year roadmap goals relatively low difficulty
  • 1. Supplement existing national or regional
    high-performance computing facilities to enable
    biomedical researchers to make optimal use of
  • 2. Develop and make accessible databases based on
    domain-specific vocabularies, ontologies, and
    data schema.
  • 3. Harden, build user interfaces for, and deploy
    on the national grid, high-throughput global
    search and analysis systems integrating genomic
    and other biomedical databases.

4-7 year roadmap goals moderate difficulty
  • 1. Develop robust computational tools and methods
    for interoperation between biomedical databases
    and tools across platforms and for collection,
    modeling, and analyzing of data, and for
    distributing models, data, and other information.
  • 2. Rebuild languages and representations (such as
    Systems Biology Markup Language) for higher level

4-7 year roadmap goals high difficulty
  • 1. Ensure productive use of GRID computing
    through participation of biologists to shape the
    development of the GRID.
  • 2. Develop user-friendly software for biologists
    to benefit from appropriate applications that
    utilize the GRID.
  • 3. Integrate key building blocks into a framework
    for the ultimate biomedical computing

8-10 year roadmap goals relatively low difficulty
  • 1. Employ the skills of a new generation of
    multi-disciplinary biomedical computing

8-10 year roadmap goals moderate difficulty
  • Produce and disseminate professional-grade,
    state-of-the art, interoperable informatics and
    computational tools to biomedical communities. As
    a corollary, provide extensive training and
    feedback opportunities in the use of the tools to
    the members of those communities.

8-10 year roadmap goals high difficulty
  • Deploy a rigorous biomedical computing
    environment to analyze, model, understand, and
    predict dynamic and complex biomedical systems
    across scales and to integrate data and knowledge
    at all levels of organization.

Initial Steps on the Roadmap Plan I
  • We have released a funding announcement, and
    received proposals, for the creation of four NIH
    National Centers for Biomedical Computing. Each
    Center is to serve as the node of activity for
    developing, curating, disseminating, and
    providing relevant training for, computational
    tools and user environments in an area of
    biomedical computing. We hope ultimately to
    establish eight centers.

Initial Steps on the Roadmap Plan II
  • We are preparing a funding announcement for
    investigator-initiated grants to collaborate with
    the National Centers. Instead of having big
    science and small science compete with each
    other, we will create an environment in which
    they will work hand in hand for the benefit of
    all science.

Initial Steps on the Roadmap Plan III
  • We are preparing a funding announcement for work
    on creating and disseminating curricular
    materials that will embed the learning and use of
    quantitative tools in undergraduate biology
    education for future biomedical researchers. We
    are committed to pressing a reform movement in
    undergraduate biology education to ensure an
    adequate number of quantitatively trained and
    able biomedical researchers in the future.

Initial Steps on the Roadmap Plan IV
  • We are in the initial stages of establishing a
    formal assessment and evaluation process. A
    possible form is that an external group of
    scientists will establish criteria by which to
    evaluate the program, and a professional survey
    research group will work with the scientists to
    implement the ongoing assessment and evaluation
    plan, so that prompt and appropriate mid-course
    corrections and tuning will take place.

Key Features of the NIH Bioinformatics and
Computational Biology Roadmap Process
  • Every component goes through NIH peer review
  • Larger components are by cooperative agreement
    rather than grant, with active continued
    participation by NIH program staff.
  • There is complete transparency about the rules
    and the process (except for the confidentiality
    necessary for peer review).
  • Assessment and Evaluation are built in from the
  • Program, review, and evaluation are independent
    of each other.

expressed in the funding announcement for this
project) There is no prescribed single license
for software produced in this project. However
NIH does have goals for software dissemination,
and reviewers will be instructed to evaluate the
dissemination plan relative to these goals 1)
The software should be freely available to
biomedical researchers and educators in the
non-profit sector, such as institutions of
education, research institutes, and government
laboratories. 2) The terms of software
availability should permit the commercialization
of enhanced or customized versions of the
software, or incorporation of the software or
pieces of it into other software packages. 3) The
terms of software availability should include the
ability of researchers outside the center and its
collaborating projects to modify the source code
and to share modifications with other colleagues
as well as with the center. A center should take
responsibility for creating the original and
subsequent "official" versions of a piece of
software, and should provide a plan to manage the
dissemination or adoption of improvements or
customizations of that software by others. This
plan should include a method to distribute other
user's contributions such as extensions,
compatible modules, or plug-ins. The application
should include written statements from the
officials of the applicant institutions
responsible for intellectual property issues, to
the effect that the institution supports and
agrees to abide by the software dissemination
plans put forth in the proposal.
Possible areas of productive interaction with
other agencies
  • with DOE on microbial science and nanoscience and
  • with DARPA on microbial science and on
    nanoscience and biotechnology
  • with USDA on nutrition and agricultural science
  • with NIST on data and software standards and on
  • with NSF on biology at all levels, on integrating
    biomedical computational science with the
    cyberinfrastructure initiative, on fostering
    interdisciplinary collaborative science, on
    nanoscience, and on biology education
  • f. with NASA and NOAA on environmental issues
    related to health

National Institute for Biomedical Imaging and
Bioengineering (NIBIB)
  • Dr. Roderic Pettigrew Director
  • Improve health through fundamental
    discoveries, design and development, and
    translation and assessment of technological
    capabilities in biomedical imaging and
    bioengineering, enabled by relevant areas of
    information science, physics, chemistry,
    mathematics, materials science, and computer

NIBIB Computation Activities
  • Biomedical Information Science and Technology
    Consortium (BISTIC)
  • Neuroinformatics
  • Human Brain Project (HBP)
  • Collaborative Research in Computational
    Neuroscience (CRCNS)
  • Neuroimaging Informatics Technology Initiative
  • Interagency Modeling and Analysis Group (IMAG)

Interagency Modeling and Analysis Group (IMAG)
  • Formed in 2003, lead by NIBIB
  • Purpose To promote modeling and analysis
    methods in biomedical systems
  • Interagency initiative on Multiscale Modeling

Interagency Modeling and Analysis Group (IMAG)
  • Participants
  • 13 NIH components (NIBIB, NIDA, NIGMS, NINDS,
    OD, and CSR)
  • 3 NSF directorates (ENG, CISE, and BIO)
  • DOD
  • NASA

NIBIB Program Areas
  • Mathematical Models and Computational Algorithms
  • Bioinformatics and Medical Informatics
  • Human-Computer Interface, Image Displays,
    Perception, and Image Processing
  • Imaging Device Development
  • Imaging Agent and Molecular Probe Development
  • Tissue Engineering
  • Biomaterials
  • Medical Devices and Implant Science
  • Therapeutic Agent Delivery Systems and Devices
  • Biosensors
  • Biomechanics and Rehabilitation Engineering
  • Platform Technologies
  • Nanotechnology
  • Remote Diagnosis and Therapy
  • Surgical Tools and Techniques

Mathematical Models and Computational Algorithms
  • Multiscale, structural and functional modeling
  • New, novel modeling methodology (nonlinear and
    systems methods)
  • Clinical decision algorithms
  • Statistical methods and data reduction models
  • Imaging algorithms (distortion correction and
    motion detection)
  • Data analysis methods
  • Tangible molecular models

  • Data acquisition, management and processing
  • Data mining and data analysis
  • Networked tools for transfer of images and
    radiological reports (GRID)
  • Digital atlases, gene expression maps,
    probabilistic maps
  • Knowledge-based reporting systems
  • Mapping and visualization of function and
    diseases (genotype and phenotype)
  • Medical informatics
  • Biostatistics

Image Processing
  • Segmentation and registration
  • Image analysis, pattern recognition,
    computer-aided diagnosis
  • Multi-modal imaging analysis (PET, MR,)
  • Neuroimaging
  • Mammography
  • Perceptual modeling
  • Dosage Radiography

Remote Diagnosis and Therapy
  • Remote-monitoring and quantification of images
  • Data and model integration for critical care
  • Wearable sensors and data fusion
  • Haptics and tele-diagnostics
  • Neurophysiological interoperative monitoring
  • Internet-based home healthcare
  • Remote-management of disease

Surgical Tools and Techniques
  • Computer-assisted surgery (Haptics)
  • Simulated surgical training
  • Image-guided interventions

Future Directions at NIBIB
  • Interagency Modeling and Analysis Group (IMAG)
  • Systems Biology/Tissue Engineering
  • Imaging Informatics
  • Data Integration
  • Large-scale Databases

NIBIB Program Contacts
  • Modeling / Bioinformatics / Neuroprosthesis /
    Telehealth Technologies / Biomechanics /
  • Grace Peng
  • Biosensors / Tissue Engineering
  • Chris Kelley -
  • Biomaterials / Nanotechnology
  • Peter Moy -
  • Bioinformatics / Imaging Informatics
  • Peter Lyster -
  • Imaging
  • John Haller -
  • Alan McLaughlin
  • Yantian Zhang
  • Training
  • Meredith Temple-OConnor -