DIVERSITY-BASED, MODEL-GUIDED CONSTRUCTION OF SYNTHETIC GENE NETWORKS WITH PREDICTED FUNCTIONS

Engineering artificial gene networks from modular components is one of the major goals of synthetic biology. However, the construction of gene networks with predictable functions remains hampered by a lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. Here we present an approach that couples libraries of diversified components (synthesized with randomized non-essential sequence) with in silico modeling to guide predictable gene network construction without the need for post-hoc tweaking. We demonstrate our approach in S. cerevisiae by synthesizing regulatory promoter libraries and using them to construct feedforward loop networks with different predicted input-output characteristics. We then expand our method to produce a synthetic gene network acting as a predictable timer, modifiable by component choice. We utilize this network to control the timing of yeast sedimentation, illustrating how the plug-and-play nature of our design can be readily applied to biotechnology.