Extending Biology

For 3.6 billion years, evolution has governed biology on this planet; now Mother Nature has a collaborator. Inexpensive tools for reading and rewriting the genetic code of life are enabling us to manipulate biology from the bottom up.

“The idea of synthetic biology is to do for biology what electrical engineers have done for circuit design and what chemists have done for the synthesis of chemicals—that is, to make an engineering field out of it,” says University of California, Berkeley, Professor Jay Keasling, director of Lawrence Berkeley National Laboratory’s Synthetic Biology Department. “Rather than just use the natural devices as they exist, we’re building new parts that we can integrate into devices that function in predictable ways.”

Keasling is already developing microbial factories to produce drugs against malaria and cancer and to spew out octane for fuel. Meanwhile, MIT researchers are building a storehouse of interchangeable parts—BioBricks—that can be snapped together to construct artificial systems in living cells. One key enabler behind synthetic biology is the ability to rapidly sequence and synthesize DNA and to do it very cheaply. Forty-two percent of our respondents believe that it will become affordable to do this for any organism in the next decade; another 38 percent think it will happen in the following decade.

What about sequencing your own genome? Fifty-six percent of our respondents forecast that most individuals in developed countries will have a documented personal genetic profile within two decades. “Medical therapies will be quite individualized,” Edward Berbari, biomedical engineering professor at Indiana University–Purdue University, Indianapolis, noted in his survey response. “These will be specific therapies for diagnosed maladies aimed at very specific metabolic pathways.” He expects such therapies in another 10 years or so.

New devices are on the horizon that would close the gap between biology and bits. Cochlear implants to treat deafness and deep brain stimulators to treat Parkinson’s disease are already on the market. Meanwhile, implantable brain-machine interfaces are making headlines with primitive artificial vision systems and mind-controlled robot prosthetics. But fewer than half of the Fellows believe that implantable brain-machine interfaces will be widely adopted.

“While technology may permit many of the forecasted accomplishments to occur, human beings may well resist their implementation,” writes electrical and computer engineering professor Andrew Szeto of San Diego State University in his survey comments.

As Yogi Berra reportedly said, “The hardest thing to predict is the future.” And as we’ve said, our survey does not try to predict the sci-tech future but merely to uncover key directions. So although we may not be able to say that in 2015 a space elevator will be shuttling goods and people into orbit or that in 2020 we’ll all have robot servants, we can foresee that in the next several decades we will be building our infrastructure in a new way: we will have unlimited computing resources, live in a sensory-rich computing environment, and reengineer ourselves and the biological world around us. Understanding these larger trends helps organizations think about adapting to the future, and thus shaping it. Alan Kay’s prescription: “The best way to predict the future is to invent it.”

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Computer Science (199 respondents)

Will a universal language translator become commercially available?

Unlikely 15.1%

Equal chances 20.1%

Likely 64.8%

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When is this likely to occur?

10 years or less 19.8%

11 to 20 years 50%

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Will a quantum computer reach the market?

Unlikely 42.7%

Equal chances 25.1%

Likely 22.1%

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Image: Mike Manzano/iStockphoto

Will handwriting recognition approach 99% accuracy?

Unlikely 15.1%

Likely 69.3%

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When is this likely to occur?

10 years or less 31.2%

11 to 20 years 46.4%

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Will computer speech recognition of unstructured human speech approach 99% accuracy?

Unlikely 19.1%

Likely 61.8%

........

When is this likely to occur?

10 years or less 25.2%

11 to 20 years 49.5%

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Will we use parallel programming in mainstream applications?

Unlikely 5%

Equal chances 9%

Likely 83.4%

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When is this likely to occur?

10 years or less 58.6%

11 to 20 years 31.6%

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