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View Presentation By Dr. Peter Lyster

  1. 1. The NIH Bioinformatics and Computational Biology Roadmap Peter Lyster PhD Program Director, Computational Biology and Bioinformatics 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
  2. 2. 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.
  3. 3. Computational Biology at the NIH— why, whence, what, whither • Why—Because 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 can’t translate what you don’t 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.
  4. 4. Computational Biology at the NIH— why, whence, what, whither • Whence—Computation 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 rule—yet it is not often cited as an example of the importance of calculating technology.
  5. 5. Computational Biology at the NIH— why, whence, what, whither • What—Today computation is at the heart of all leading edge biomedical science. For leading examples, consider this past year’s Nobel prizes: • Structure of voltage-gated channels—required 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 channels—The experimental work required augmentation by bioinformatics for identification of water channel genes by sequence homology. • Magnetic resonance imaging—A large share of the prize work was for the mathematical and computational techniques for inferring structure and image from NMR spectra.
  6. 6. Computational Biology at the NIH— why, whence, what, whither Institutes and Centers at NIH support substantial development and implementation of computation and information technology embedded in biomedical research. Informatics is a key component of the NIH Roadmap Initiative
  7. 7. Roadmap Activities: Computation – New Pathways to Discovery • National Centers for Biomedical Computing • Building Blocks, Biological Pathways, and Networks – 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)
  8. 8. 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.
  9. 9. 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.
  10. 10. Some important problems with biomedical computing tools are: • They are difficult to use. • They are fragile. • They lack interoperability of different components • 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.
  11. 11. Computational Biology at the NIH—why, whence, what, whither • Whither—The 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 organization.”
  12. 12. 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 enhanced. • 8. Enhance training opportunities in bioinformatics and biomedical computing.
  13. 13. 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.
  14. 14. 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 them. • 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.
  15. 15. 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 function.
  16. 16. 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 environment.
  17. 17. 8-10 year roadmap goals: relatively low difficulty • 1. Employ the skills of a new generation of multi-disciplinary biomedical computing scientists
  18. 18. 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.
  19. 19. 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.
  20. 20. 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.
  21. 21. 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.
  22. 22. 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.
  23. 23. 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.
  24. 24. Key Features of the NIH Bioinformatics and Computational Biology Roadmap Process • Every component goes through NIH peer review system. • 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 start. • Program, review, and evaluation are independent of each other.
  25. 25. SOFTWARE DISSEMINATION REQUIREMENTS FOR NIH NATIONAL CENTERS FOR BIOMEDICAL COMPUTING. (As 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.
  26. 26. Possible areas of productive interaction with other agencies a. with DOE on microbial science and nanoscience and biotechnology b. with DARPA on microbial science and on nanoscience and biotechnology c. with USDA on nutrition and agricultural science d. with NIST on data and software standards and on nanoscience e. 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
  27. 27. 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 sciences.
  28. 28. 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 (NIfTI) • Interagency Modeling and Analysis Group (IMAG)
  29. 29. 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
  30. 30. Interagency Modeling and Analysis Group (IMAG) • Participants • 13 NIH components (NIBIB, NIDA, NIGMS, NINDS, NCI, NIMH, NHGRI, NCRR, NICHD/NCMRR, NLM, NIEHS, OD, and CSR) • 3 NSF directorates (ENG, CISE, and BIO) • DOD •DARPA •TATRC • NASA
  31. 31. NIBIB Program AreasNIBIB 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
  32. 32. 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
  33. 33. Bioinformatics • 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
  34. 34. Image Processing • Segmentation and registration • Image analysis, pattern recognition, computer- aided diagnosis • Multi-modal imaging analysis (PET, MR,…) • Neuroimaging • Mammography • Perceptual modeling • Dosage – Radiography
  35. 35. 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
  36. 36. Surgical Tools and Techniques • Computer-assisted surgery (Haptics) • Simulated surgical training • Image-guided interventions
  37. 37. – Interagency Modeling and Analysis Group (IMAG) – Systems Biology/Tissue Engineering – Imaging Informatics – Data Integration – Large-scale Databases Future Directions at NIBIB
  38. 38. NIBIB Program Contacts Modeling / Bioinformatics / Neuroprosthesis / Telehealth Technologies / Biomechanics / Rehabilitation 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-O’Connor -