Researchers from the Massachusetts Institute of Technology (MIT), Boston University, and the National Institute of Standards and Technology (NIST) have demonstrated a powerful tool for programming DNA to encode new functions into living cells. The advance automates the design of genetic circuits, which process chemical signals rather than electronic ones.
As described in the April 1 issue of Science, the team created and tested a programming language to compile high-level functional specifications into DNA sequences that could be inserted into bacterial cells, giving them new sensing and response capabilities. Future applications for this kind of programming include designing bacterial cells that can produce a cancer drug when they detect a tumor or creating yeast cells that can halt their own fermentation process if too many toxic byproducts build up.
Until now, designing and making genetic parts — such as sensors or switches — and customizing cells to function as programmable factories have been arduous, trial-and-error tasks.
Using the MIT-developed DNA-programming language — called Cello, for cellular logic — the researchers programmed 60 circuits with different functions, and 45 of them worked correctly the first time they were tested. Many of the circuits were designed to measure one or more environmental conditions, such as oxygen level or glucose concentration, and respond accordingly. Another circuit was designed to rank three different inputs and then respond based on the priority of each one.
One of the new circuits is the largest biological circuit ever built, containing seven logic gates and about 12,000 base pairs of DNA.
"It would take years to build these types of circuits. Now you just hit the usa-button and immediately get a DNA sequence to test," Christopher Voigt, an MIT professor of biological engineering, explained in an MIT news release.
NIST collaborators used a fluorescence-microscopy method to quantify the signal strength of the genetic circuits. The NIST approach, which entails counting individual RNA molecules, converts measurements of signal strength into standard units that make it possible to compare measurements done in different labs.
"Ultimately, our goal is to develop a suite of measurement tools that can be used across labs and enable predictive engineering of organisms," said David Ross, leader of NIST's Engineering Biology Team.
Read the MIT news release.