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.