CPS:Synergy: Safety-Aware Cyber-Molecular Systems
Iowa State University, Ames IA
Investigators
Abstract
Cyber-molecular systems are networks of nanoscale physical devices in which computation controls the behavior and activities of the molecular devices. In some cyber-molecular systems the programmable controller itself is implemented by DNA gates and algorithms. Many of the planned future uses of cyber-molecular systems are safety-critical, such as biosensors to detect pollutants in water or disease markers in the body, and drug therapeutics to deliver a customized medicine only to the locations in the body where the disease has been detected. The problem is how to design cyber-molecular systems to be safe for use in a dynamic and only partially understood physical environment. This project addresses this problem by introducing a new model of chemical reaction networks that can be implemented robustly. The class of computations accepted by the model can be implemented in such a way that they will provably perform correctly even when crucial physical parameters are perturbed by small amounts. The project will leverage this advance to establish an end-to-end safety-aware perspective for creating cyber-molecular systems. The approach entails tightly integrated computational and physical risk analyses; advances in reaction-network-based modeling and verification techniques; and model-driven experimentation and evaluation on prototyped cyber-molecular systems. This project has the potential to make future cyber-molecular systems safer. Results also will provide capabilities in risk analysis and automated verification to meet the need for future certification of safety-critical cyber-molecular systems, such as biosensors and drug therapeutics. More broadly, the new model of biochemical reaction networks will advance understanding of how to analyze very large sets of nanoscale cyber-physical systems that operate in a probabilistic physical environment from a safety perspective. This project will also focus on interdisciplinary training of a diverse student population, and results will be integrated into undergraduate and graduate courses.
View original record on NSF Award Search →