Data-Driven Chassis Selection for Fieldable Synthetic Biology
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Synthetic Biology has the potential to address a myriad of problems...
Despite its promise, solutions to many of these problems will require using Synthetic Biology systems outside of the laboratory, in other words, making synthetic biology fieldable.
Currently, there are several roadblocks that prevent synthetic biology systems from becoming fieldable, that is, able to be implemented in an environmental setting or in situ in an organism.
When designing a fieldable system, effective chassis selection is the initial barrier that a researcher must overcome. The choice of chassis impacts every aspect of circuit design and implementation.
Despite the importance of computational tools to other aspects of synthetic biology and the vast amount of available 16S data…
…software tools to assist researchers in the rational, data-driven selection of chassis for a specific environment outside the laboratory are lacking.
To address this pressing need, the 2022 William & Mary iGEM team has developed and implemented a novel, data-driven, model-based software program, chassEASE, to assist researchers in selecting an optimal bacterial chassis for a soil, air, water, or human gut microbiome environment.