Quantitative analysis and simulation of cellular processes is becoming an important and indispensible tool for predictive biomedical research. Not only that, but models are becoming larger, more sophisticated and encompassing areas such as multicellular and multiscale modeling of tissues and whole organs. Performance can be improved by using compute clusters or the cloud, however interactive computing is severely restricted using this kind of infrastructure. Moreover supercomputing or cloud access is not available to everyone. In this proposal we wish to focus on developing high performance simulators, notably libRoadRunner and specific hardware based on analog computing. libRoadRunner is unique in that it is the only cellular simulator that compiles standard SBML using LLVM. LLVM is a backend machine code generation technology that is starting to be widely used by performance conscious software developers. Using this technology we have achieved significant, in some cases orders of magnitude speed ups in simulation times compared to existing simulators. Our letters of support highlight some of the major improvements that have been realized by this new technology. We have shown that the performance of libRoadRunner is on par (within 95%) with natively compiled solutions. In other words we have reached the limit to computing on ordinary desktop computers. In this proposal we will continue to improve libRoadRunner and implement a completely novel way to carry out cellular simulations which will lead to additional orders of magnitude improvements in simulation performance. Our proposal is to work with Rahul Sarpeshkar at Dartmouth who has pioneered hardware based simulation technologies called cytomorphic computing. As part of this proposal, the Dartmouth group will develop a new generation of ultra-high-speed hardware-based biochemical simulators. We will develop SBML and MATLAB translators that can translate a model directly into programmable high-performance cytomorphic silicon chips. In addition to this highly innovative approach we will also exploit this hardware to implement novel approaches to interactive real-time modeling of cellular processes that includes the complete control of time and temporal incursion. A small part of the proposed effort will include general maintenance of our existing infrastructure as well as organizing an annual workshop on modeling and training.

Public Health Relevance

Systems biologists use modeling to understand both disease and non-diseased states. Of particular interest are researchers who model tissues or whole organs which imposes considerable demands on computing power given the model size and complexity. In this work we will develop and explore extremely high performance but cost effective computing solutions to enable researchers and pharmaceutical companies to model large systems or repeated modeling in reasonable time thereby decreasing the cost of research and development.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Brazhnik, Paul
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University of Washington
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
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Choi, Kiri; Medley, J Kyle; K├Ânig, Matthias et al. (2018) Tellurium: An extensible python-based modeling environment for systems and synthetic biology. Biosystems 171:74-79
Kim, Jaewook; Woo, Sung Sik; Sarpeshkar, Rahul (2018) Fast and Precise Emulation of Stochastic Biochemical Reaction Networks With Amplified Thermal Noise in Silicon Chips. IEEE Trans Biomed Circuits Syst 12:379-389
Woo, Sung Sik; Kim, Jaewook; Sarpeshkar, Rahul (2018) A Digitally Programmable Cytomorphic Chip for Simulation of Arbitrary Biochemical Reaction Networks. IEEE Trans Biomed Circuits Syst 12:360-378
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