Dynamic CRISPRa/i regulation of Gene expression in CFS and E.coli

Abstract

Effective control of gene expression underlies many modern biotechnological applications from metabolic engineering to diagnostics and bio-computation. Historically, a paucity of orthogonal and engineerable regulators has stymied efforts to increase the scale and complexity of gene regulatory networks. The development of CRISPR-based transcriptional regulators has enabled the generation of increasingly sophisticated and practical regulatory networks. The advent of transcriptional networks operating through the regulated expression of guideRNAs has further expanded the composability of CRISPR-regulation, allowing guideRNAs to serve as a standardized information carrier, greatly simplifying the level matching process in multi-layer circuits. Despite these rapid advances, until recently effective CRISPR-regulation in prokaryotes has been limited to CRISPR inhibition due to the lack of versatile activating domains. To meet the needs of increasingly ambitious undertakings we have developed a prokaryotic CRISPRa/i control system programmable through the regulated expression of guideRNAs. In this work we first establish design principles allowing the formation of multi-layer CRISPRa/i regulatory circuits to provide complex and dynamic control of gene expression. To improve upon CRISPRa/i network function we subsequently engineered expression characteristics of CRISPRa/i nodes to provide increased output dynamic ranges, enabling formation of CRISPRa/i expression programs with increased complexity. Finally, we discuss the application of this modular control system to provide dynamic regulation of gene expression in CFS and E.coli towards optimization of bioproduction. The dynamic regulatory capabilities afforded by the CRISPRa/i control system greatly expands the design space of genetically encoded expression programs. This expanded set of capabilities will enable the rapid generation of genetically encoded, dynamic, multi-gene programs providing access to new avenues for the optimization of metabolic engineering as well as complex signal processing in biological systems.