The networks of genes in each animal is a bit like the network of neurons in our brains, which suggests they might be “learning” as they go.
Many people will be familiar with the idea that genes are passed from parent to offspring, and those genes that help their hosts survive and reproduce have a better chance of getting passed on.
Instead, the impact of this filtering allows gene networks in animals to actually “Learn” what works and what does not over time.
“Gene networks evolve like neural networks learn,” he says.
Watson’s basis for this claim is the idea that the connections between genes can be strengthened or weakened as a species evolves and changes — and it is the strength of those connections in gene networks that allow organisms to adapt.
If an organism has certain genes firing together in this way, and that organism proves successful enough to reproduce, then its offspring will not simply inherit its beneficial genes, argues Watson.
Watson suggests analysing how gene networks change in microbes that evolve in the lab.
There are already some existing features of gene networks that help stand up Watson’s approach.
A mini-network of genes defining a particular adaptation — like one of the modules mentioned above — can sometimes be turned on or off by just one other activator gene.
Originally published at Cogly.
