Computational Synthetic Biology: Progress and the Road Ahead
Synthetic biology promises to leverage engineering principles to enable model-based design of genetic circuits. In model-based design, practitioners exploit computational models and analysis tools to efficiently explore the design space before producing a physical artifact. Unfortunately, creating such a model-based design approach for synthetic biology has proven to be very challenging. The goal of this talk is to both highlight the progress in the development of computational methods to support synthetic biology and the remaining challenges that must be overcome before synthetic biology can realize its full potential. In particular, there are three critical aspects for model-based design for synthetic biology: (1) standards, (2) abstraction, and (3) decoupling. This talk will present recent results using standards, such as the Synthetic Biology Open Language (SBOL) to create repositories of well-characterized components that can reliably be used and re-used. This talk will also present abstraction methods that leverage efficient analysis techniques, such as stochastic model checking, to enable designers to efficiently explore the design space for large, complex designs. Finally, this talk will present results on developing automated synthesis techniques inspired from digital electronic circuit design that have the potential to decouple concerns of design from those of fabrication. While much work remains to be done for computational methods to truly have impact on experimental synthetic biology, collaborations between computational and experimental synthetic biologists have the potential for tremendous benefits in the areas of health, energy, and the environment.