|Munson, Matthew S
|Ross, David J.
|Salit, Marc L.
|Strychalski, Elizabeth A
Synthetic Biology will be successful as a manufacturing technology--and will be a commercial force--when it can be engineered in a scalable manner. An attractive strategy for scaling an engineered technology is to base it on modular functional elements that can be reused in multiple contexts and repurposed in diverse applications. Such a “kit” of standard synthetic biology “parts” that perform their functions predictably can be used to hide complexity and enable high level design. Ideally, the kit will free the designer from managing low level functionality and fabrication, so development can take place with an abstract design vocabulary, with plug-and-play reuse, replication, and the ability to simply create complex functions.
Collaborative research between Stanford University and NIST is being initiated to create the standards and new measurement capabilities that will make this “plug-and-play” architecture of reusable genetic elements possible. Research topics include development of (1) a “model-able” cell that can be used as a test breadboard for genetic elements; (2) standardized design rules and constraints needed for plug-and-play, including approaches to standardize operational parameters for predictable performance; (3) measurement approaches, including reference standards and materials, to assess interactions of parts across changing contexts, and to evaluate parts design approaches that minimize (or predictably exploit) such interactions; (4) standardized parts characterization, to establish a predictable universe of interoperable parts; (5) techniques to measure critical performance parameters of parts, including rates/fluxes and activities, and their dependencies; and (6) standardized fabrication rules that enable a diverse ecosystem of genetic materials manufacturing, with transferrable methods amongst facilities.
Postdoctoral researchers are being solicited to develop work in these areas, conducting this work at both Stanford University in Palo Alto, CA and NIST in Gaithersburg, MD.
Synthetic biology; Metrology; Metabolic Engineering; Predictive models; Genetic circuits; Standards in biology; In vivo measurement; Theoretical models;