Scaling computation and memory in living cells

Abstract

The semiconductor revolution that began in the 20th century has transformed society. Key to this revolution has been the integrated circuit, which enabled exponential scaling of computing devices using silicon-based transistors over many decades. Analogously, decreasing costs in DNA sequencing and synthesis, along with the development of robust genetic circuits, are enabling a “biocomputing revolution”. First-generation gene circuits largely relied on assembling various transcriptional regulatory elements to execute digital and analog computing functions in living cells. Basic design rules and computational tools have since been derived so that such circuits can be scaled in order to implement complex computations. In the past five years, great strides have been made in expanding the biological programming toolkit to include recombinase- and CRISPR–based gene circuits that execute complex cellular logic and memory. Recent advances have enabled increasingly dense computing and memory circuits to function in living cells while expanding the application of these circuits from bacteria to eukaryotes, including human cells, for a wide range of uses.

Measurement of large serine integrase enzymatic characteristics in HEK293 cells reveals variability and influence on downstream reporter expression

“…For instance, Hartley, et al developed the λ integrase for rapid and efficient plasmid construction, widely known as gateway cloning [10][11][12]. Recombinases have also been used for creating genetic programs capable of integrating information from various inputs [13][14][15][16][17] and recording biological events genetically [18]. This technology can be seen in the study of mouse genetics, by using the Cre-mouse system coupled with selective promoters to selectively turn on or off gene expression in specific tissues [19].…”

A synthetic distributed genetic multi-bit counter

“…With the growing importance of engineering synthetic genetic circuits, we are currently facing scalability bottlenecks caused mainly by the inability of host cells to endure “large” foreign circuits. Due to the limitations of traditional recombinase-based state machines ( Chiu and Jiang, 2017 ; Yehl and Lu, 2017 ), the CRISPRi system is being explored more actively to encode cellular-based memory ( Yehl and Lu, 2017 ; Andrews et al., 2018 ). Our counter framework can be readily used with CRISPRi for implementing more complex circuits in host cells and can potentially realize additional functions per cell ( Jusiak et al., 2016 ).…”

A framework for designing miRNA-based distributed cell classifier circuits

“…programs implemented in living cells were successfully studied before (Moon et al, 2012). Also, computing systems with increasing complexity operating in living cells seem to become available (Yehl and Lu, 2017;Lapique and Benenson, 2018). As research in synthetic biology moves in the direction of multi-device computing systems implemented in living cells presented work may provide valuable insights into their design.…”