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No, the World's Health Problems Won't Be Solved With an App

Sorry, amateur biohackers.
(Photo: Whichy/Shutterstock)

(Photo: Whichy/Shutterstock)

As some people tell it, the biotech industry will soon work like Silicon Valley. Last week the journal Naturedescribed Canadian futurist Andrew Hessel's vision for biotech, which sounds a lot like the sharing-economy business model of Uber and Airbnb: "What if volunteer researchers, working cooperatively from their garages and bedrooms, could rival the efforts of multibillion-dollar pharmaceutical companies?"

Hessel's vision is a noble one. With his Pink Army Cooperative, Hessel aims to give people "a cancer treatment just for you, in a day, for free." This will happen, he argues, because biotech is on the cusp of the equivalent of the personal computing revolution: "Until the mid-1970's, [computers] remained big, expensive machines only available to elite groups. Then the microprocessor changed everything by making computers personally affordable.... Today, it is biotechnology that is poised to go personal. And one of the first 'killer apps' will be the things that kill us, like cancer." Technology is not the barrier to innovation, Hessel argues, it's the stifling, hierarchical culture of heavy regulation and giant, profit-seeking corporations.

When you're developing a new app, you can run it on your laptop to see how it works; if you're engineering a new microbe, you may need a $100,000 mass spectrometer to test your work.

There is no question that the computer industry was transformed by the work of inspired amateurs. Personal computing really was developed, in part, by brilliant college drop-outs who worked in garages. Mark Zuckerberg dropped out, too, after inventing Facebook in his Harvard dorm. The appealing democratic ethos and stunning successes of talented outsiders in the end-user-oriented computer industry makes it easy to think that this is how innovation should work everywhere. Why not biotech? Will a student working in a garage come up with a cancer-killer app?

At first glance, it's easy to think so. The recent advances in computing and electronics have made biological research much more accessible. Anyone with an Internet connection can access huge databases of genomic data, read open access scientific literature, get lab troubleshooting advice in online forums, and buy inexpensive kits to do basic molecular biology. Each year, in a competition sponsored by the International Genetically Engineered Machine Foundation (iGEM), teams of students around the world build prototypes of engineered organisms that sound like they could solve major world problems. What's stopping anyone with a good biotech idea from trying it?

The problem is that, despite appearances, engineering biology is still very, very hard. Comparisons between biotech and the computer industry are misplaced; for the medium-term future at least, serious biological engineering will remain the province of those with highly specialized training and access to expensive equipment. Why?

Unlike learning a programming language or application development environment, you can't learn biology by picking up an O'Reilly manual, firing up your terminal application, and coding “Hello World.” To function competently in bioengineering, like most other engineering fields, you need some mastery of a very broad and complex knowledge base. Along with an intellectual understanding of biology, tacit knowledge—know-how that is acquired by practice—is absolutely crucial but, by nature, not conveyed very easily by books or lectures. Lab biology has to be learned in-person, despite efforts to make it easy. The bioengineering company Ginko Bioworks offers a how-to manual that tells users how to make their own engineered organisms with off-the-shelf molecular biology parts. The manual includes some very basic techniques that are learned by every graduate student in molecular biology, yet even with that knowledge, experienced scientists will often spend weeks troubleshooting the problems that crop up routinely with these methods.

Aside from knowledge, bioengineering often requires access to specialized and expensive equipment and supplies that let you measure the results of your engineering efforts. When you're developing a new app, you can run it on your laptop to see how it works; if you're engineering a new microbe, you may need a $100,000 mass spectrometer to test your work.

Last week's report of a bacteria engineered to have an expanded genetic code illustrates why biological engineering is so hard. To create a bacteria with extra letters in its DNA “alphabet,” the team of Scripps Institute scientists relied on expert chemical knowledge to choose the new DNA letters. They used radioactive materials and expensive equipment to determine whether their project was working. Even with resources and expertise, the final product was the result of years of full-time lab work. And despite the technical elegance of the work, it’s not yet a killer app: There are currently very few useful applications for these engineered bacteria. This work is typical of the entire field: Bioengineering is hard not because our institutions are flawed, but because the technical challenges are still huge.

In this month's issue of Nature Methods the editors put it bluntly: "The field has not yet reached the point where genetic parts can be predictably combined to achieve a desired outcome." The world's best biological engineers agree that "we do not yet know enough biology to make synthetic biology a predictable engineering discipline." When it comes to creating elegant designs with DNA, "we are just learning to hold the pencil."

This is bad news for would-be amateur biohackers who want to solve important problems with a killer biotech app. But Silicon Valley isn't the only model for how to innovate. A better comparison for today's biotech is the state of personal computing’s oft-overlooked uncle, computer engineering, in the 1950s. Although computers then were only accessible to academic and corporate institutions with deep pockets, researchers made important innovations that make up the foundation of almost all computing today. Cutting-edge biotech is currently limited to corporations and universities that employ teams of Ph.D.s for now, and that means it largely reflects the interests, thinking, and ethics of insiders with access to special skills and resources. But the innovations that are happening now are laying crucial groundwork that will make biological engineering more tractable and more accessible to talented amateurs in the future.