On 10 April, astrophysicists announced that they had captured the first ever image of a black hole. This was exhilarating news, but none of the giddy headlines mentioned that the image would have been impossible without open-source software. The image was created using Matplotlib, a Python library for graphing data, as well as other components of the open-source Python ecosystem. Just five days later, the US National Science Foundation (NSF) rejected a grant proposal to support that ecosystem, saying that the software lacked sufficient impact. It’s a familiar problem: open-source software is widely acknowledged as crucially important in science, yet it is funded non-sustainably.
Computerized genetic-design tools automate the process by which researchers design complex genetic circuits that can program cells — especially bacteria and yeast — to carry out specific actions, such as activating a particular enzyme or churning out a certain protein. Synthetic biologists have used single-celled organisms in this way to produce drugs, biological sensors that include cells or antibodies, enzymes for use in industry, and more.
On a cold morning in Minneapolis last December, a man walked into a research centre to venture where only pigs had gone before: into the strongest magnetic resonance imaging (MRI) machine built to scan the human body. First, he changed into a hospital gown, and researchers made sure he had no metal on his body: no piercings, rings, metal implants or pacemakers. Any metal could be ripped out by the immensely powerful, 10.5-tesla magnet — weighing almost 3 times more than a Boeing 737 aeroplane and a full 50% more powerful than the strongest magnets approved for clinical use. “This is a window we’ve just never had in the intact human brain,” says Ravi Menon.
Debilitating hand pain is always bad news, but Harold Pimentel’s was especially unwelcome. As a computational-biology PhD student, his work involved constant typing — and he was born with only one arm. “My adviser jokingly said, ‘Can’t you do this by voice?’” he recalls. Three years later, as a computational-genomics postdoc at Stanford University in California, he does just that.
Sometimes it’s hard to understand someone else’s research through a static scientific paper. Across countless universities and companies, at whiteboards and cafeteria tables, you’ll find scientists in animated conversations explaining their research to one another, asking questions, playing around with each other’s data: in short, interacting. Across the internet in recent years, people have extended these explanations to include interactive graphics and code.
Now a web-only machine-learning journal called Distill aims to provide a formal home for these interactive graphical explanations.
This is shaping up to be the year of DNA for cryptocurrency. One startup after another is offering to pay you in bitcoin-like tokens for sharing your genetic data.
But it’s hard to see how using blockchains and cryptocurrencies will substantially increase demand for genome sequencing. That’s a vexing problem because too few genomes have been sequenced and analyzed to generate as many meaningful insights as scientists had hoped.
Aviv Regev likes to work at the edge of what is possible. In 2011, the computational biologist was collaborating with molecular geneticist Joshua Levin to test a handful of methods for sequencing RNA. The scientists were aiming to push the technologies to the brink of failure and see which performed the best. They processed samples with degraded RNA or vanishingly small amounts of the molecule. Eventually, Levin pointed out that they were sequencing less RNA than appears in a single cell.
To Regev, that sounded like an opportunity. The cell is the basic unit of life and she had long been looking for ways to explore how complex networks of genes operate in individual cells, how those networks can differ and, ultimately, how diverse cell populations work together. The answers to such questions would reveal, in essence, how complex organisms such as humans are built.
Every day in the U.S., about 22 people die waiting for an organ transplant. If scientists could 3-D print organs like kidneys, livers and hearts, all those lives could be saved. For years, people have been touting personalized organ printing as the future.
But despite decades of promising work in bioengineered bladders and other kinds of human tissue, we’re not close to having more complicated organs made from scratch. Harvard professor Jennifer Lewis, a leader in advanced 3-D printing of biological tissue, has only recently developed the ability to print part of a nephron, an individual unit of a kidney.
I asked Lewis what it will take to someday print a full kidney or a similarly complex organ.