A team from the University of Pennsylvania’s Perelman School of Medicine has discovered a way to develop antibiotic chemicals by making use of powerful new techniques in machine learning. Turning to one of the most exciting branches of computing science let the team show big data really could be the medical savior many have been predicting, and that it could help even the odds in man’s ongoing fight against the evolution of killer microbes.

At any given time in a cell, which genes are being expressed? For a long time, researchers thought answering this question would solve an enormous array of problems in science and medicine. Why wouldn’t it? After all, just sequencing the genome didn’t turn out to be the medical super-cure we were all hoping for — but if we understand when those sequences are actually being used to control the cell, surely then we’ll realize at least a portion of the promise of genomic analysis. Right?

bacteria2To an extent, yes. Next-generation genome analysis, which includes the ability to see which genes are being put to use at which times, has allowed a battery of new therapies to be created. But these therapies generally derive from time consuming, old-style medical experiments and, more importantly, they usually involve relatively few individual genes. Big data can currently bring genes to scientists’ attention, show that it is implicated in some process very generally, but its ability to show exactly how that gene is leading to its effect is still fairly low.

One of the big reasons is visible in the video below, which visualizes gene expression in a malaria parasite. Can you pick out which genes are being expressed in which combinations, in response to which other genes? Yeah, neither can scientists, for the most part.

So, to a great extent the challenge is to “de-noise” genetic information — and that’s where the computational technique ADAGE comes in. It stands for Analysis using De-noising Autoencoders of Gene Expression, and it’s about, well, that. De-noising is a technique in machine learning that lets researchers find patterns in dirty data, with a bunch of confounding signals that, while they might be part of some pattern, are not a part of the particular pattern we care about.

That’s what these scientists did with a number of strains of Pseudomonas Aeruginosa, a problem bacteria associated with cystic fibrosis, among other diseases. Their relatively simple neural network model, which is about as complex as a brain with only a few dozen neurons, was able to use ADAGE to identify related genes, both those that enhance and inhibit each others’ effects. However, it also unexpectedly noticed the differences between the team’s chosen bacterial strains, identifying those that were taken from the lungs of patients as distinct from the lab standard. It did this by looking solely at their relative patterns of gene expression.

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The most dangerous weapon of all?

Note that this is so potentially powerful because it’s such a starkly different approach from the historical experiments that led us to this point. In the past, researchers basically worked in the opposite direction: some observable characteristic of the cell is tracked to the protein causing the observation, to the gene encoding the protein, to the specific pattern of activity that allows that gene to have that effect. In this case, researchers observe the activity patterns without context, then brute-analyze them to find other genes, with known effects. This allows them to work forwards toward practical effects on the cell, rather than backward from them.

What this means is that, in the fight against antibiotic-resistant bacteria, a computational approach could figure out complex, interdependent genetic systems without researchers having already identified those genes as part of the problem system. More to the point, if the innovation that allows some important resistance gene is actually in the pattern of gene expression rather than the activity of each gene itself (as many are), this approach could identify them.

Overall, I am a somewhat guilty denier of the prophesied antibiotic apocalypse. It’s not that the issue of bacterial evolution isn’t real, but that I think as the threat becomes more and more impossible to ignore, we will quite quickly bring humanity’s incredible abilities in molecular and data analysis to bear on it. And, at the risk of sounding like the guy who called the Titanic unsinkable, I just really think the modern research sector is smarter than bacterial evolution — even when that evolution is driven by trillions upon trillions of antibiotics-powered genetic dice rolls.