May 23rd, 2012 | Category: Computing, Information processing, Media coverage, Papers, Synthetic Biology | Leave a comment

Data storage in living cells

There’s a lot of interest (including at the Beeb) in a new paper from Drew Endy published in PNAS: rewritable digital data storage in live cells via engineered control of recombination directionality. According to Drew Endy,

One of the coolest places for computing is within biological systems.

There was an iGEM project from Paris Liliane Bettencourt 2010 (‘every bacteria counts’) around a similar theme for those interested in biocomputing. And here’s the abstract from the PNAS paper,

The use of synthetic biological systems in research, healthcare, and manufacturing often requires autonomous history-dependent behavior and therefore some form of engineered biological memory. For example, the study or reprogramming of aging, cancer, or development would benefit from genetically encoded counters capable of recording up to several hundred cell division or differentiation events. Although genetic material itself provides a natural data storage medium, tools that allow researchers to reliably and reversibly write information to DNA in vivo are lacking. Here, we demonstrate a rewriteable recombinase addressable data (RAD) module that reliably stores digital information within a chromosome. RAD modules use serine integrase and excisionase functions adapted from bacteriophage to invert and restore specific DNA sequences. Our core RAD memory element is capable of passive information storage in the absence of heterologous gene expression for over 100 cell divisions and can be switched repeatedly without performance degradation, as is required to support combinatorial data storage. We also demonstrate how programmed stochasticity in RAD system performance arising from bidirectional recombination can be achieved and tuned by varying the synthesis and degradation rates of recombinase proteins. The serine recombinase functions used here do not require cell-specific cofactors and should be useful in extending computing and control methods to the study and engineering of many biological systems.

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