A Tool-Chain Approach to Predictive Design of Biological Circuits
There is a pressing need for design automation tools for synthetic biology systems. Compared to electronic circuits, cellular information processing has more complex elementary components and a greater complexity of interactions among components. Moreover, chemical computation within a cell is strongly affected both by other computations simultaneously occurring in the cell and by the cell's native metabolic processes and its external environment. This complexity implies an engineering workflow that is currently highly iterative, error-prone, and extremely slow---critical problems that must all be addressed in order to realize the potential of synthetic biology.
The TASBE tool-chain integrates techniques from compilation, constraint solving and digital circuit design with fluid handling robotics and new protocols for DNA assembly, factoring he problem of design and assembly into sub-problems that can be more readily solved. Practitioners using our tool-chain are able to design organisms using high level behavior descriptions, which are automatically transformed into genetic regulatory network designs, then assembled into DNA samples ready for in vivo execution. The tool-chain is also free and open software, which will allow researchers to incorporate their own design tools, thereby disseminating their results to the community and enhancing the capabilities of the tool-chain.
Early results are promising, with the tool-chain automatically generating designs whose behavior in vivo can be quantitatively predicted from the transfer curves for individual DNA parts.
Dr. Jacob Beal is a scientist at BBN Technologies, a research affiliate of MIT CSAIL, and a Science Commons Fellow. His research interests center on the engineering of robust adaptive systems, with a focus on problems of modelling and control for spatially-distributed systems like sensor networks, robotic swarms, and natural or engineered biological cells. Dr. Beal completed his Ph.D. in 2007 under Prof. Gerald Jay Sussman at the MIT Computer Science and Artificial Intelligence Laboratory.