CCF-FET: Small: Modular CRISPRi/a programs by resource feedback regulation

CRISPR-based gene regulation is enabling scientists to build advanced genetic systems that work as computers, and carry out tasks such as sensing signals, counting, and making logic decisions. This could enable living cells to perform useful jobs, such as detecting disease and producing drugs, by responding to their environment in programmed ways. For these complex designs to work, each part needs to operate independently, without interfering with the others. This is currently difficult to achieve partially because all parts rely on the same limited cellular resources. When multiple components compete for those resources, they can interfere with each other, making the system unpredictable. Adding a new piece can disrupt what was already working. To address this, this project aims to first predict how this competition affects system behavior and then design feedback control mechanisms that automatically balance resource use, allowing genetic circuits to function more reliably and at scale. Models that predict the effects of resource loading on the functionality of CRISPRi/a based genetic circuits do not currently exist. Yet, these are required to engineer CRISPR-based genetic programs that behave as intended. Currently, such models only exist for CRISPRi or CRISPRa, separately; however, the combination of the two is essential in a variety of genetic circuits, such as memory storage elements, required for sequential logic. Feedback regulators have been developed only for dCas9, but not for the activator protein domain necessary in CRISPRa. Furthermore, the design of combined feedback regulators for both resources is challenging due to possible interference between them. This project has activities spanning three specific objectives. In objective 1, predictive models of genetic circuits built out of CRISPRi/a modules will be created, which account for resource loading. These will allow to design genetic circuits with more predictable behavior. In objective 2, concurrent feedback regulators for both dCas9 and the activator protein domain will be designed to neutralize competition in CRISPRi/a circuits. In objective 3, the methods developed in Objectives 1 and 2 will be showcased on the design of a biological Flip-Flop, the fundamental memory element in sequential logic, which can store two information states and can be set/reset by its inputs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2520358 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Domitilla Del Vecchio | Institution: Massachusetts Institute of Technology, CAMBRIDGE, MA | Award Amount: $592,620 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2520358 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2520358.html