Biological circuit design gets complex

CAMBRIDGE, Mass.—The idea of programming has a long history, from training animals (or people) to getting machines to do routine tasks and computers to calculate and analyze. More recently, programming has found its way into life sciences, as researchers have used synthetic biology to program cells so that they can elicit responses such as fluorescing in response to a specific chemical or producing drugs when the programmed cells come across certain disease markers. Now, engineers at the Massachusetts Institute of Technology (MIT) have upped the ante with programmable cells that can remember and respond to a series of events, according to a recent MIT article.

This could have significant implications in studying cellular processes that involve some specific series or order of events, and in tracking disease progression or response to therapeutics.

“[We] developed a scalable recombinase-based strategy for implementing state machines in living cells, in which state is encoded in DNA sequence. The direct storage of state information in the DNA sequence ensures that it is maintained stably and with minimal burden to the cell,” the study states.

The programmable cells can remember as many as three different inputs which, in turn, can lead to as many as 16 separate states, but researchers believe they will be able to increase inputs in the near future. Currently scientists are able to track cellular events in order of their occurrence, program cellular trajectories and store the history of the cell’s movements.

“You can build very complex computing systems if you integrate the element of memory together with computation,” said Timothy Lu, an associate professor of electrical engineering and computer science and of biological engineering and head of the Synthetic Biology Group at MIT’s Research Laboratory of Electronics.

In the study, published in the July 22 issue of Science, senior author Lu and colleagues used E. coli cells that they programmed to respond to substances like ATc, arabinose and DAPG. Once they had circuits that could record events and the series in which these events occurred, scientists integrated genes and genetic regulatory elements into the array of recombinase binding sites. Thus they were able to use the state machine circuits to both record information and control whether genes were turned on or off.

The scientists used genes that code for fluorescing proteins—red, blue and green—to test their biological circuits. The order in which cells fluoresced would then show the inputs received, and in what order they were received. For example, if the cells were coded for input A followed by input B, they fluoresced red and green. Cells that were coded for input B before input A fluoresced red and blue.

The researchers now want to use their biological circuits to further study cellular processes that are influenced by a certain order or series of events, such as the appearance of signaling molecules or the activation of certain genes.

“This idea that we can record and respond to not just combinations of biological events but also their orders opens up a lot of potential applications. A lot is known about what factors regulate differentiation of specific cell types or lead to the progression of certain diseases, but not much is known about the temporal organization of those factors. That’s one of the areas we hope to dive into with our device,” said Nathaniel Roquet, an MIT and Harvard graduate student and the paper’s lead author.

The MIT team noted that in the future, scientists may be able to use the state machine platform to track stem cells into mature cell types, or follow the progression of diseases, like cancer mutations, or how cancer cells respond to treatments.

As the researchers noted in the Science paper, “Such state machines may also improve our understanding of disease progression, which can also depend on the appearance and order of extracellular and intracellular factors. For example, in cancer, the temporal order of genetic mutations in a tumor can determine its phenotype. Similarly, in both somatic diseases and pathogenic infections, pre-adaptation of disease cells to different environmental conditions may affect the way the cells behave and respond to drug treatments. Integrating state machines into disease models and subsequently analyzing the history of cells that survive treatment would be useful for understanding how disease progression affects therapeutic response.”

In other recent cell programming news from MIT, researchers in March shared an open source programming language called Cello, for designing complex circuits that program new functions into living cells.

“It is literally a programming language for bacteria,” explained Christopher Voigt, an MIT professor of biological engineering. “You use a text-based language, just like you’re programming a computer. Then you take that text and you compile it and it turns it into a DNA sequence that you put into the cell, and the circuit runs inside the cell.”

It doesn’t take a biologist’s know-how to use the Verilog-based programming language; part of the reason for creating Cello was to enable non-biologists to quickly design their own working systems. The programming language takes text and translates it into workable DNA sequences.

Researchers have programmed cells with several different functions. Some are programmed to measure things like oxygen levels and then respond based on their findings. Others are programmed to rank multiple inputs based on priority.

The advantage of this technique is speed: until now, Voigt said, “it would take years to build these types of circuits. Now you just hit the button and immediately get a DNA sequence to test.”