Scalable platforms for computation and memory in living cells

Abstract

Living cells are biological computers - constantly sensing, processing and responding to biological cues they receive over time and space. Devised by evolution, these biological machines are capable of performing many computing and memory operations, some of which are analogous to and some are distinct from man-made computers. The ability to rationally design and dynamically control genetic programs in living cells in a robust and scalable fashion offers unprecedented capacities to investigate and engineer biological systems and holds a great promise for many biotechnological and biomedical applications. In this thesis, I describe foundational platforms for computation and memory in living cells and demonstrate strategies for investigating biology and engineering robust, scalable, and sophisticated cellular programs. These include platforms for genomically-encoded analog memory (SCRIBE - Chapter 2), efficient and generalizable DNA writers for spatiotemporal recording and genome engineering (HiSCRIBE - Chapter 3), single-nucleotide resolution digital and analog computing and memory (DOMINO - Chapter 4), concurrent, autonomous and high-capacity recording of signaling dynamics and events histories for cell lineage mapping with tunable resolution (ENGRAM - Chapter 5), continuous in vivo evolution and synthetic Lamarckian evolution (DRIVE - Chapter 6), tunable and multifunctional transcriptional factors for gene regulation in eukaryotes (crisprTF - Chapter 7), and an unbiased, high-throughput and combinatorial strategy for perturbing transcriptional networks for genetic screening (PRISM - Chapter 8). I envision the platforms and approaches described herein will enable broad applications for investigating basic biology and engineering cellular programs.