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What if genes aren't entirely digital?

An unpublished letter to Nature magazine.

Sheila Jasanoff and J. Benjamin Hurlbut, in their call for a gene editing laboratory (Nature 555, 435; 2018) stress the "tendency to fall back on the framings that those at the frontiers of research find most straightforward and digestible". My concern is that the most digestible framing of all – that genes are editable in the first place – is gravely misleading.

The telling metaphor of genes-are-code arose in the 1950s at the coincidental dawns of genetics and computer science. Codons (combinations of DNA base pairs) map precisely onto different amino acids according to the so-called genetic code, which is nearly universal for all life on Earth. And thus, sequences of base pairs form genes, which "code" for proteins and enzymes, in what look beguilingly like programs specifying organisms. But while the low level genetic code is neat and digital, what happens further up the biochemical stack is much more analogue. Proteins and enzymes are never single purpose, and never play their roles within a body in isolation. Genes unlike computer instructions are not compartmentalised; genomes unlike computer programs are not designed one at a time, but have evolved as intricate ensembles, with selection pressures operating between bodies, and between species.

The genes-are-code metaphor should have been re-examined as genetics evolved. The decidedly non-computer-like reality has been betrayed over time by discoveries like interactive gene expression, epigenetics, and the sobering fact that non-coding "junk DNA" is not junk after all. One wonders if applied biology was held up for decades by the simplistic presumption that DNA had to code for something in order to be functional.

If the genome is not really digital, then "hacking" it like code will inevitably have unintended consequences. It's not just the public which needs a better understanding of genetic engineering but the mislabelled "engineers" themselves.

April 14, 2018.

Posted in Software engineering, Science

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