Scaling Computation and Memory in Living Cells
Name
nihms917370.pdf
Description
Accepted version
Size
1.2 MB
Format
Adobe PDF
Checksum (MD5)
8e52b9b4b5bd6aaeaaaa7f26763ef0f6
Author(s)
Yehl, Kevin
Lu, Timothy
Date Issued
2017
Journal
Current Opinion in Biomedical Engineering
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2017 Elsevier Inc. 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.
MIT Department
Massachusetts Institute of Technology. Synthetic Biology Center
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.COBME.2017.10.003
