Originally published July 5, 2017
Here is an excerpt:
But here’s the key difference: When the robots do finally discover the genetic changes that boost chemical output, they don’t have a clue about the biochemistry behind their effects.
Is it really science, then, if the experiments don’t deepen our understanding of how biology works? To Kimball, that philosophical point may not matter. “We get paid because it works, not because we understand why.”
So far, Hoffman says, Zymergen’s robotic lab has boosted the efficiency of chemical-producing microbes by more than 10%. That increase may not sound like much, but in the $160-billion-per-year sector of the chemical industry that relies on microbial fermentation, a fractional improvement could translate to more money than the entire $7 billion annual budget of the National Science Foundation. And the advantageous genetic changes that the robots find represent real discoveries, ones that human scientists probably wouldn’t have identified. Most of the output-boosting genes are not directly related to synthesizing the desired chemical, for instance, and half have no known function. “I’ve seen this pattern now in several different microbes,” Dean says. Finding the right genetic combinations without machine learning would be like trying to crack a safe with thousands of numbers on its dial. “Our intuitions are easily overwhelmed by the complexity,” he says.
The article is here.