Design and Selection of a Synthetic Feedback Loop for Optimizing Biofuel Tolerance.

Feedback control allows cells to dynamically sense and respond to environmental changes. However, synthetic controller designs can be challenging because of implementation issues, such as determining optimal expression levels for circuit components within a feedback loop. Here, we addressed this by coupling rational design with selection to engineer a synthetic feedback circuit to optimize tolerance of Escherichia coli to the biojet fuel pinene. E. coli can be engineered to produce pinene, but it is toxic to cells. Efflux pumps, such as the AcrAB-TolC pump, can improve tolerance, but pump expression impacts growth. To address this, we used feedback to dynamically regulate pump expression in response to stress. We developed a library with thousands of synthetic circuit variants and subjected it to three types of pinene treatment (none, constant, and varying pinene). We were able to select for strains that were biofuel tolerant without a significant growth cost in the absence of biofuel. Using next-generation sequencing, we found common characteristics in the designs and identified controllers that dramatically improved biofuel tolerance.

Performing selections under dynamic conditions for synthetic biology applications.

As the design of synthetic circuits and metabolic networks becomes more complex it is often difficult to know a priori which parameters and design choices will result in a desired phenotype. To counter this, rational design can be complemented by library-based approaches where diversity is introduced and then coupled with screening or selection methods. Here, we used a model of competitive growth to show that selection can rapidly identify library variants with near-optimal phenotypes. Many synthetic biology applications require phenotypes that balance multiple objectives, such as responding to more than one chemical signal. In addition, desired traits may be time-dependent, for example changing with the growth phase. By applying dynamic inputs to the selection, we show that it is possible to select for traits that satisfy multiple goals. Furthermore, we demonstrate that the underlying diversity in a library is heavily influenced by the initial circuit design. Overall, our findings argue that rational synthetic circuit design, coupled with diversity generation and dynamic selection are powerful tools for many synthetic biology applications.