If biologists could put computational controls inside living cells, they could program them to sense and report on the presence of cancer, create drugs on site as they’re needed, or dynamically adjust their activities in fermentation tanks used to make drugs and other chemicals.

Now researchers at Stanford University have developed a way to make genetic parts that can perform the logic calculations that might someday control such activities.

The Stanford researchers’ genetic logic gate can be used to perform the full complement of digital logic tasks and it can store information too. It works by making changes to the cell’s genome, creating a kind of transcript of the cell’s activities that can be read out later with a DNA sequencer. The researchers call their invention a “transcriptor” for its resemblance to the transistor in electronics.

“We want to make tools to put computers inside any living cell—a little bit of data storage, a way to communicate, and logic,” says Drew Endy, the bioengineering professor at Stanford who led the work.

Timothy Lu, who leads the Synthetic Biology Group at MIT, is working on similar cellular logic tools. “You can’t deliver a silicon chip into cells inside the body, so you have to build circuits out of DNA and proteins,” Lu says. “The goal is not to replace computers, but to open up biological applications that conventional computing simply cannot address.”

Biologists can give cells new functions through traditional genetic engineering, but Endy, Lu, and others working in the field of synthetic biology want to make modular parts that can be combined to build complex systems from the ground up. The cellular logic gates, Endy hopes, will be one key tool to enable this kind of engineering.

Cells genetically programmed with a biological “AND” gate might, for instance, be used to detect and treat cancer, says Endy. If protein A and protein B are present—where those proteins are characteristic of, say, breast cancer—then this could trigger the cell to produce protein C, a drug.

In the cancer example, says Endy, you’d want the cell to respond to low levels of cancer markers (the signal) by producing a large amount of the drug. The case is the same for biological cells designed to detect pollutants in the water supply—ideally, they’d generate a very large signal (for example, quantities of bright fluorescent proteins) when they detect a small amount of a pollutant.

The transcriptor triggers the production of enzymes that cause alterations in the cell’s genome. When the production of those enzymes is triggered by the signal—a protein of interest, for example—these enzymes will delete or invert a particular stretch of DNA in the genome. Researchers can code the transcriptor to respond to one, or multiple, different such signals. The signal can be amplified because one change in the cell’s DNA can lead the cell to produce a large amount of the output protein over time.

Depending on how the transcriptor is designed, it can act as a different kind of logic gate—an “AND” gate that turns on only in the presence of two proteins, an “OR” gate that’s turned on by one signal or another, and so on. Endy says these gates could be combined into more complex circuits by making the output of one the input for the next. This work is described today in the journal Science.

MIT’s Lu says cellular circuits like his and Endy’s, which use enzymes to alter DNA, are admittedly slow. From input to output, it can take a few hours for a cell to respond and change its activity. Other researchers have made faster cellular logic systems that use other kinds of biomolecules—regulatory proteins or RNA, for example. But Lu says these faster systems lack signal amplification and memory. Future cellular circuits are likely to use some combination of different types of gates, Lu says.

Christopher Voigt, a biological engineer at MIT, says the next step is to combine genetic logic gates to make integrated circuits capable of more complex functions. “We want to make cells that can do real computation,” he says.

Image via iStock, palau83

This article originally published at MIT Technology Review here