Toward Useful Biomedical Models

1. Toward UsefulBiomedical Models IMAG Grace C.Y. Peng, Ph.D. The 2ndBiosupercomputing Symposium “Toward integrative understanding of the live phenomena, current status and the future” Tokyo, Japan March 19. 2010

2. This Talk Thoughts from NIH Interagency Modeling and Analysis Group (IMAG) IMAG Futures Meeting Report Opportunities for global collaboration Funding Opportunities

3. The NIH The NIH is an agency within the Department of Health and Human Services within the executive branch of the US government. Its mission is science in pursuit of fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to extend healthy life and reduce the burdens of illness and disability. The NIH is the primary federal agency for conducting and supporting medical research in the US.

4. NIH Congressional Appropriations (in billions) The recovery act Provided $10.4 billion to NIH for use in FYs 09-10. $40.7 NIH Budget Doubling

5. NIH Director’s 5 Opportunities for Research • Opportunity 1: HIGH THROUGHPUT TECHNOLOGIES • Next Generation DNA sequencing • Nanotechnology • Small molecule screening • New imaging modalities • Computational biology • Opportunity #2:TRANSLATIONAL MEDICINE • Public-Private Partnerships • small molecules for drug trial • stem cells

6. NIH Director’s 5 Opportunities for Research • Opportunity #3: BENEFITING HEALTH CARE REFORM • Comparative effectiveness research • Prevention and personalized medicine – behavioral research • Health disparities research • Pharmacogenomics • Health research economics • Opportunity #4: FOCUSING MORE ON GLOBAL HEALTH • Infectious, neglected tropical, chronic noncommunicable diseases and injuries • Opportunity #5:REINVIGORATING AND EMPOWERING THE BIOMEDICAL RESEARCH COMMUNITY • Innovation and Training

7. IMAG Interagency Modeling and Analysis Group

8. IMAG Futures MeetingDecember 15-16, 2009 Google -- IMAG Futures The archived interactive Videocast, all meeting presentations (Agenda), reports, and public commentary (Feedback) are publicly available on the IMAG wiki Purpose of IFM: Discuss the IMPACT of biomedical, biological and behavioral modeling

9. Models are used everywhere Except…

10. IFM Meeting Structure - 5 major levels of the biological hierarchy 1) Population 2) Whole-Body 3) Cell-Tissue-Organ 4) Pathways and Networks 5) Atomic and Molecular Diverse representation of expertise and fields

11. Meeting Charge IMPACT -- success stories ACCEPTANCE –- emerging trends CHALLENGES –- what didn’t work & why? OPPORTUNITIES – how far can we go?

12. The take home message from this meeting… To make an impact on the broader biomedical communities… Models must be useful

13. IMPACT !! In search of the “killer app”

14. IMPACT -- success stories • Population • Archimedes model – health coverage decisions, clinical guidelines, policy and research planning • Gail and Claus models – breast cancer risk • BRCAPro – genetic counseling • Epidemic infectious disease models – HIV, BSE/CJD, UK Foot and Mouth (2001), small pox, measles, H1N1 pandemic • New discipline of “pharmacometrics” – model-based drug development

15. IMPACT -- success stories • Whole-Body • Predicting risk of bone fracture • Predicting risk of aneurysm rupture • Pediatric oncology • FDA artificial pancreas

16. GOOGLE: FDA Spotlight CPI

17. IMPACT -- success stories • Cell-Tissue-Organ • Hodgkin and Huxley – predicted existence of ion channels  discovery of channelopathies • Understanding physiological mechanisms • transmission of force through the skeletal system • pressure and flow in the cardiovascular system • how heart ejection fraction depends on tissue structure • Pathways and Networks • Cell cycle dynamics • Engineered gene regulatory networks • Understanding immune system dynamics

18. IMPACT -- success stories • Atomic and Molecular • Molecular modeling and drug development • HIV drug cocktails – models of inhibitors of the viral enzymes • SARS virus inhibitor • Potent thrombin inhibitors for blood clotting diseases (Merck) • glaucoma treatment Dorzolamide • migraine medication Zolmitriatan • Viagra (initially developed for hypertension and then angina) • Notable herbicides and fungicides were also developed by QSAR techniques

19. ACCEPTANCE –- emerging trends Are models driving research and policy?

20. ACCEPTANCE –- emerging trends • Population • Successes tied to: • tight integration with data and biological expertise • publication of results (ideally parallel teams with a variety of modeling assumptions) • efforts to engage the people who shape policy • Advent of “pharmacometrics” tied to availability of computer-intensive modeling tools • Correlated with the availability of data • Encouraged by modeling in a consortia

21. ACCEPTANCE –- emerging trends • Whole-Body • When simulations predict a novel physiological mechanism • Presentation of model to experimentalists • Simple functional models for conveying ideas • Emphasis on what the model can accomplish • Complex models for integrating data and hypothesis • Overcome learning and communication barriers • Hands-on experience with the model

22. ACCEPTANCE –- emerging trends • Cell-Tissue-Organ • Recognition that more complex systems require systematic analysis through models • Application of engineering and physics principles to understanding physiological mechanisms • New structural and functional data driving new models • Models driving data collection (e.g. the Connectomics initiative and the Cell Centered Database) • Models driving technology development • Multi-disciplinary investigators working together in same laboratory or team

23. ACCEPTANCE –- emerging trends • Cell-Tissue-Organ (con.) • Multiscale modeling driving research • Electrophysiology • Mechanobiology • Experimental biomechanics • Inverse analysis (e.g. electrocardiographic imaging) is coming into clinical use • Computational neuroscience • Image-driven modeling

24. ACCEPTANCE –- emerging trends • Pathways and Networks • Models presented with clarity and with meaning to the experimentalists • Both communities recognize the model’s utility and limitations • Synthesis of conceptual and quantitative models • Understanding and management of cancer - integrate and explore large “omic” datasets with clinical information • Atomic and Molecular • Strong culture for community developed codes

25. CHALLENGES –- what didn’t work & why? How do we move forward?

26. COMMUNICATIONin multidisciplinary research 1. Clinicians, biologists and modelers speak in different languages 2. Clinicians and biologists may not know what is technically possible; modelers may not know the biomedical problems. 3. Continued, integrated collaboration essential

27. CHALLENGES • Population • Clinical and biological realism • Population diversity • Fully utilizing observational data • Whole-Body • Clinical use of models bring issues of conflict of interest, IP, etc.

28. CHALLENGES • Cell-Tissue-Organ • Tools to enhance understanding of biological complexity • Conceptual models  Quantitative models • Integration of large heterogeneous datasets with legacy data and concepts • Standardized protocols and experimental instrumentation and techniques for improved data collection and analysis

29. CHALLENGES • Cell-Tissue-Organ (con.) • Starved for data at the meso-scale • Lack of detailed physical properties to assign to physical structures at the cell, tissue and organ scale that can now be reconstructed with unprecedented resolution • Model assumptions must reflect the patient population and the differences between distinct and diverse patient populations • Extending multi-scale models in time (disease progression and aging)

30. CHALLENGES • Pathways and Networks • Mapping these models with spatially realistic models? • When to use different approaches – (stochastic, single compartment, continuum methods) • Adapting models to large-scale multi-core systems • Atomic and Molecular • Educate broadly RE: model limitations and failures • Too many parameters to identify with confidence • Quantitative calculation of uncertainty • Model sharing

31. OPPORTUNITIES – how far can we go? What is our modeling moonshot?

32. OPPORTUNITIES – how far can we go? • Population • Link between biological and clinical scales/stages • Whole-Body • Clinical work flows and standard operating procedures • Validate simulations with clinical data • Cell-Tissue-Organ • Pharmaceutical drug discovery research • Patient-specific diagnosis and procedure planning

33. OPPORTUNITIES – how far can we go? • Pathways and Networks • Cross-fertilization of ideas between data rich and data poor fields • A“realistic” model of a “complete” cell – are we there yet? • Atomic and Molecular • Show predictability and quantifiable computing • Understand systematic error and invalidation to improve model assumptions

34. OPPORTUNITIES – General comments • Predict new findings or mechanisms and generate new hypotheses as well as reproduce known phenomena • Uncertainty Quantification - understanding range of validity of model, or where the model fails • Validation - quantitative experimental confirmation of model results, transparency to the user

35. OPPORTUNITIES – General Comments • Communicate to convey useable model and useable data (standards) - DEMYSTIFY • Demonstrate the greater capability of the model to integrate complex data • Integrate mathematical modeling in the training of biomedical scientists • Integrating models across scales • Cross-fertilization between fields

36. OPPORTUNITIES – General Comments • Multiscale models for device design and regulatory submissions – in vivo boundary conditions for safety and efficacy • Reference data and models for regulatory validation and evaluation • Comparative effectiveness research? • Integrate modeling into all areas of biomedical science

37. Conclusions from the IFM “a model can be viewed as an extension of the mind” particularly when the data is vast and diverse, and the mind can not fully process its complexity in order pass judgment toward a decision the model provides a framework for capturing the complexity and quantifying uncertainty modeling in all biomedical fields is inevitable…

38. It’s all about IMPACT NIH definition for reviewers likelihood for the project to exert a sustained, powerful influence on the research field(s) involved Models must be useful to make an impact

39. Genes Cellstructure-function Tissuestructure-function Organstructure-function Clinical medicine mRNA Proteins Lipids Carbohydrates … 4 tissue types 30,000+ genes 100,000+proteins 200+ cell types 12 organsystems 1 body Courtesy of Peter Hunter Multiscale Modeling Multi-scale modeling deals with spanning scales from molecular to population and is expected to largely impact the understanding of biological processes and also further the predictive capability in biological, biomedical and environmental systems. Multi-scale modeling encompasses concepts of space, time and state space. Biological Scales

40. Interagency Modeling and Analysis Group (IMAG)Wiki(Google: IMAG Wiki) Participation Welcome!

41. Participation Welcome!

42. The MSM Consortium provides opportunities to: converse with program officers from 9 government agencies, IMAG Participants, from the United States and Canada network with other MSM investigators, MSM Participants and Projects participate in Working Group discussions on the wiki participate in virtual scientific presentations by all Working Groups throughout the year participate in annual meetings of the MSM Consortium, IMAG/MSM Events learn about the latest modeling and MSM related activities from around the world, Multiscale Modeling of the Physiome - Projects Around the World access various Resources for Modeling

43. NSF – Office of CyberinfrastructureSoftware Infrastructure for Sustained Innovation (SI2) • Create a software ecosystem that scales from individual or small groups of software innovators to large hubs of software excellence Focus on innovation Focus on sustainability

44. GOOGLE: SI2 Dear

45. X Sensor Nets Contact: Manish Parashar mparasha@nsf.gov www.nsf.gov/oci Infrastructure (XD) Data Archives (DataNet) Data Archives (DataNet) Visualization/Analytics Experiments/Instruments

46. Funding Opportunity - PAR 08-023Predictive Multiscale Models of the Physiome in Health and Disease (R01) To develop multiscale models that link at least two biological scales of modeling and accurately predict biomedical and behavioral processes in health and disease states To develop predictive, multiscale models that link to higher levels of the physiome. Models must link to at least one scale beyond molecular interactions at the cellular level (e.g. to cell-cell, cell-environment interactions, as listed above) To develop predictive, multiscale models that are physiologically mechanistic and have translational potential or clinical applicability To bring together modeling and biomedical expertise to collaborate on building multiscale model(s) To validate and test models with standard datasets To develop models that can be explicitly shared with other modelers

47. どうもありがとう! Grace C.Y. Peng penggr@mail.nih.gov