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Sim. TK: software for physics-based simulation of biological structures Michael Sherman, Chief Software Architect Sim. TK: software for physics-based simulation of biological structures Michael Sherman, Chief Software Architect Simbios NCBC at Stanford NCBC Software and Data Integration Meeting 8 July 2005, North Bethesda, MD

Topics • • Scope of Simbios Center & Sim. TK SW Architecture of Sim. Topics • • Scope of Simbios Center & Sim. TK SW Architecture of Sim. TK Computational considerations Opportunities for software & data integration • Licensing approach 2

Simbios mission Enable biomedical scientists to use, develop and share accurate models & simulations Simbios mission Enable biomedical scientists to use, develop and share accurate models & simulations of biological structures—from molecules to organisms. Strategy: – Develop, disseminate, support an open-source “biosimulation toolkit” Sim. TK – Support several “driving biological problems” to ensure accuracy & utility 3

Predict structures & co-structures MDM 2/p 53 Complex Formation • Proteins • RNA • Predict structures & co-structures MDM 2/p 53 Complex Formation • Proteins • RNA • Larger complexes • Molecular machines Imagiro® Molecular Dynamics Courtesy Locus Pharmaceuticals 4

Scaling up: Molecular motors Conceptual visualization R. A. Milligan Myosin/actin interaction 5 Assembled from Scaling up: Molecular motors Conceptual visualization R. A. Milligan Myosin/actin interaction 5 Assembled from EM data Walker, et al. , Nature 405 (2000)

Simulation – a size problem Calmodulin “morph” ~1200 atoms David Parker (Simbios) 6 Myosin Simulation – a size problem Calmodulin “morph” ~1200 atoms David Parker (Simbios) 6 Myosin coarse simulation ~50, 000 atoms (yellow blobs are calmodulin)

Scaling down • Large mechanical models are oversimplified • Need to incorporate more details, Scaling down • Large mechanical models are oversimplified • Need to incorporate more details, fine structure • Biomaterials are very complex Knee implant 7 Knee!

Neuromuscular F. C. Anderson, M. Pandy 8 J. Teran, E. Sifackis, C. Lau, R. Neuromuscular F. C. Anderson, M. Pandy 8 J. Teran, E. Sifackis, C. Lau, R. Fedkiw

Simulation-Based Medical Planning (Cardiovascular) Patient-specific models constructed from diagnostic imaging data 9 Computer simulations Simulation-Based Medical Planning (Cardiovascular) Patient-specific models constructed from diagnostic imaging data 9 Computer simulations of blood flow to evaluate alternate treatments Charles Taylor (Simbios)

Some goals for Sim. TK • Integrate field of physics-based modeling in biomedicine • Some goals for Sim. TK • Integrate field of physics-based modeling in biomedicine • Avoid duplication of effort • Permit multiscale modeling • Accelerate research in – Biocomputation – Biomodeling – Laboratory biology – Medicine 10

Necessary math for biosimulation Initial Value Problems Boundary Value Problems • ODE • elliptic Necessary math for biosimulation Initial Value Problems Boundary Value Problems • ODE • elliptic PDEs • time-varying PDE – Diffusion, fluid flow, nonlinear elastic response • Algebraic constraints (DAE) • Discrete events • Monte Carlo sampling Algebraic • Solve linear/nonlinear systems • Minimizations/optimizations • Local/global search • Kinematics 11 – (quasi)statics, electrostatics • ODE – Path fitting/planning Characteristics • Continuous/discrete • Differential/algebraic/difference • Stiff/nonstiff/stochastic • Linear/nonlinear • Combinations/hierarchies • Huge systems!

Not much of a simplification! • Biostructure simulation is general simulation (HPDAE) • Similar Not much of a simplification! • Biostructure simulation is general simulation (HPDAE) • Similar situation with other “subsets” – Mechanical systems – Electronics • Sim. TK has to help – build correct models – deliver them in “biological” applications – permit user to execute them efficiently • Must serve several distinct user communities 12

Sim. TK User Communities 1. Algorithm inventor 2. Modeler 3. Scientist/clinician But: generality is Sim. TK User Communities 1. Algorithm inventor 2. Modeler 3. Scientist/clinician But: generality is not a benefit to a specialist! So … 4. Application developer 13

How to focus? • • 14 Low hanging fruit? Pressing needs? How can we How to focus? • • 14 Low hanging fruit? Pressing needs? How can we attract initial users? What can we offer soon?

Sim. TK Tactics • Separate “modeling” from “computation” • Support many small, purpose-built, narrow Sim. TK Tactics • Separate “modeling” from “computation” • Support many small, purpose-built, narrow applications • Build centralized infrastructure at Sim. TK. org 15

Sim. TK Top-level Architecture Applications Problem solving Modeling Physics, mathematics, logic Computation Resource management Sim. TK Top-level Architecture Applications Problem solving Modeling Physics, mathematics, logic Computation Resource management Sim. TK. org • “Buy in” at several levels 16

Sim. TK. org Source. Forge–like “federated” model (uses GForge) Applications Modeling Computation Sim. TK. Sim. TK. org Source. Forge–like “federated” model (uses GForge) Applications Modeling Computation Sim. TK. org 17

SDI for Sim. TK. org • Familiar Source. Forge federated model via GForge – SDI for Sim. TK. org • Familiar Source. Forge federated model via GForge – Friendly seach, browse, download & install for end users – Self-governing projects within a defined framework • Best-in-class project hosting – – Sub. Version source code control CMake/Dart 2 for multiplatform build/test Installation & download support Mailing lists, project management, etc. • Inviting to collaborators at different levels of “openness” – private open binaries open source open development – Control over who can access what when • Peer review/certification for SW & people • Curation is a major task and great service 18

SDI for Applications Layer • Goal: best computational methods delivered to end-users through narrow, SDI for Applications Layer • Goal: best computational methods delivered to end-users through narrow, domain-specific, researcherfriendly GUIs • Issues – Curation & quality control – Delivery • Tools – Easy to combine elements into a narrowlyfocused app – Exploit available hardware for speed – Example apps for developers 19 Applications Modeling Computation Sim. TK. org

SDI for Modeling Layer • Goal: robust, shareable models • Issues – Many • SDI for Modeling Layer • Goal: robust, shareable models • Issues – Many • Model building from imaging is possible SDI area – But not the current bottleneck • A later discussion … 20 Applications Modeling Computation Sim. TK. org

Computation Layer Goals • • Support both production & research Reliable, best-of-class numerical methods Computation Layer Goals • • Support both production & research Reliable, best-of-class numerical methods Interchangeable components Encapsulation and hiding of computational expertise • Exploit parallel hardware • Put high performance in the hands of bench scientists & modelers 21

Hardware focus • Avoid impossible problems! – For now tackle technical, not people problems Hardware focus • Avoid impossible problems! – For now tackle technical, not people problems – Sharing is hard (big cluster, grid) • Sole ownership by researcher/clinician – – Off-the-shelf notebooks, PCs, small clusters Windows, Mac, Linux Effective use of dual- and dual-dual core machines Goal: end-user sees 10 X speedup for $20 K • Typical node – 4 tightly coupled 64 -bit CPUs (e. g. 2 X dual core Opterons) – Acceleration via GPUs (e. g. 2 X NVIDIA Quadro) – $12 K now, much cheaper soon 22

Scientists are used to instruments Mass Spec Biostructure simulation 23 Scientists are used to instruments Mass Spec Biostructure simulation 23

SDI for Computation: HPC • High Performance Computing – Physics-based, multiscale, computationally intense – SDI for Computation: HPC • High Performance Computing – Physics-based, multiscale, computationally intense – E. g. , fusion simulation, astrophysics, climate modeling – Similar computational structure across domains, incl. biology Applications Modeling Computation Sim. TK. org • Focus: how to speed up a single computation using parallel hardware • Extensively pursued by DOE (DARPA & NSF too) • Much available public domain software & expertise – DOE “ACTS” collection – Common Component Architecture (CCA) 24

Licensing • Anything we write or fund: open source, BSD-like license – Anyone can Licensing • Anything we write or fund: open source, BSD-like license – Anyone can do anything; just don’t blame us! • Commercial involvement is crucial to long-term health and broad dissemination! • Contributors have legitimate reasons for privacy – Welcome at all levels; easy to open up once in Sim. TK • Open source ≠ open development! – Much great software already exists in physics-based simulation – Reliable access to it is a huge problem 25

Acknowledgments • Simbios – Executive Team: Russ Altman, Scott Delp, Jeanette Schmidt, David Paik Acknowledgments • Simbios – Executive Team: Russ Altman, Scott Delp, Jeanette Schmidt, David Paik – Sim. TK staff: Clay Anderson, Ayman Habib, Jack Middleton, Bryan Keller, Chris Bruns, Pete Loan – Collaborators: Michael Levitt, Vijay Pande, Ron Fedkiw, Charles Taylor, Oussama Khatib – Students & postdocs: Silvia Blemker, Joey Teran, David Parker, James Warren • DOE – Radu Serban, Ben Allan, Rob Armstrong, David Bernholdt • Funded through NIH Roadmap for Medical Research, grant U 54 GM 072970 – Program officer: Peter Lyster – Lead science officer: Peter Highnam 26

Thank you! Contact information – – – 27 Center: simbios. stanford. edu Software: simtk. Thank you! Contact information – – – 27 Center: simbios. stanford. edu Software: simtk. org Journal: journal. simtk. org Magazine: Biomedical. Computation. Review. org Sherm: [email protected] edu