CAGEN: Critical Assessment of Genetically Engineered Networks
The Critical Assessment for Genetically Engineered Networks (CAGEN) is a competition intended to drive new approaches to designing robust, synthetic biological circuits. The competition involves teams of established researchers designing circuits that implement a given function and the assessment of their circuit's performance across a set of multiple operating environments. More information is available on the CAGEN web site (on OpenWetWare).
We propose to develop and prototype a new competition designed to improve the robustness and performance of human-designed biological circuits and devices operating in cells. The Competitive Assessment for Genetically Engineered Networks (CAGEN, pronounced "cajun") will bring together leading research groups in biological circuit design to compete to demonstrate their abilities at designing circuits that perform in a prescribed manner in a variety of cellular contexts. Each year, a steering committee will propose a challenge problem that involves the design of an increasingly complex set of biological functions in a range of environments. Teams must submit their sequences, plasmid DNA implementing their circuit and data characterizing the performance of their system against a specified test suite. The top 3-5 designs will be submitted to the NSF BIOFAB (run by Adam Arkin and Drew Endy) for final characterization, and the winner will be selected based on a set of quantifiable metrics.
As part of this proposal, we plan to implement one iteration of the competition, including selecting the challenge problem, implementing a set of reference test protocols, announcing and publicizing the competition, implementing the selection process and choosing a winner. If successful, we believe that the competition can be proposed for continued funding from other sources and that over the medium term (5-10 years) CAGEN could lead toward a more robust set of biological design methods that allow human-designed circuits and devices to perform at levels closer to their biological counterparts.
- Performance Metrics for a Biomolecular Step Response, Shaunak Sen and Richard M. Murray. Conference on Decision and Control (CDC), 2012.