Genetic and Computational Tools for Engineering Complex Prokaryotic Systems

Prokaryotic systems are promising platforms for bioproduction, but the complexity of their gene regulatory and metabolic networks presents significant challenges for engineering efficient biosynthetic pathways. While CRISPR-Cas technologies have revolutionized genome editing in eukaryotes, the development of programmable, multi-layer transcriptional control in bacteria is still emerging. This work outlines our efforts to expand the bacterial CRISPR toolkit and develop predictive models that enable dynamic and rational design of prokaryotic systems. We begin with a review of the diverse CRISPR-Cas systems available for gene editing and regulation in prokaryotes, emphasizing recent innovations that offer precise and flexible control over gene expression. Building on this foundation, we engineered interconnected CRISPRa/i circuits capable of executing dynamic genetic programs in both cell-free and bacterial systems, showing that these circuits can be layered to achieve temporal regulation and logic-based behaviors. To increase circuit complexity, we designed synthetic promoters that can be selectively activated by CRISPRa and are tunable by various inputs, including light, peptides, and small molecules—enabling the creation of large, multi-input transcriptional networks. Finally, we developed automated workflows for constructing large-scale kinetic models using proteomic and metabolomic data, which allow for prediction of system-wide metabolic responses and the identification of promising biosynthetic pathways. Together, these advances provide a versatile framework for programming sophisticated, multi-gene functions in prokaryotes, accelerating the development of next-generation microbial platforms for bioproduction.

Author dc:creator

• Alba Burbano, Diego

Advisor dc:contributor.advisor

• Carothers, James M

• Bioengineering
• Chemical engineering

Statement dc:rights

• CC BY

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1773/53448

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/53448