Tech Showcase: Ribocomputing

One goal of synthetic biology is to engineer life to recognize desired inputs and in turn respond with desired outputs. Biocomputing is the management of this input/output system, designing genetic parts that allow life to perform logic based decisions in a manner not dissimilar to the computer you're using to read this article.
Researchers at the Wyss Institute for Biologically Inspired Engineering and Arizona State University, in a recent article in Nature, demonstrated a novel method of programming cells to operate like computers. They call their method ribocomputing because their engineered cells carry out complex logic-based computations exclusively using ribonucleic acids, more commonly known as RNA.
Unlike the digital computers ubiquitous in the modern world, which use electricity to accomplish all higher-order functions, these ribocomputers perform logical operations on biological materials, such as proteins, toxins, and immune system molecules. Adding programmability to cells opens up exciting new possibilities for ways to control cells and their interactions with organisms and their environment. The ribocumputing researchers propose that using this new technique, cells and microorganisms can be programmed to accomplish tasks ranging from disease diagnostics and therapeutic drug delivery to green energy production and environmental cleanup.
In their ribocomputing demonstration, the research team engineered the bacteria E. coli to sense the presence of 12 different molecules, and then use a computational circuit encoded in RNA to calculate the correct level of green fluorescent protein (GFP) to express. GFP is commonly used as a marker in biological experiments, as its green glow makes it simple for researchers to assess the behavior of genes or pathways in question.
In the ribocomputing experiment, the researchers first designed an RNA circuit to map different combinations and levels of input molecules to specific GFP intensities, as controlled by expression levels. Next, the researchers introduced controlled levels of the input molecules into the engineered E. coli’s cellular environments, and then checked whether GFP was lighting up at the specified levels. By confirming that GFP was behaving in a manner that they expected, the researchers demonstrated the viability of their RNA circuits to carry out computations.
In general, the principles in this experiment can be applied to other kinds of bio-computation. Using the same methodology, cells can be engineered to respond with specified behavior to any number of the diverse chemical arrangements they encounter in their complicated environments.
The demonstration of successful RNA-based computing represents a significant advancement for cellular computing technology. Although such computing had been described in publications almost two decades ago, the conventional methods required resources and included drawbacks that made the techniques unattractive to pursue for commercial, industrial, and clinical applications. In previous experiments, researchers used combinations of DNA, RNA, proteins, and other molecules to design biological circuits. Incorporating these disparate computing elements into a circuit design is more error-prone: successful operation of the circuit would hinge on the precise coordination of these multifarious components. Each component adds a source of noise to the logical circuit, degrading the results. As the computational load increases, it can limit the effectiveness and applicability of the method. By using RNA only, various sources of noise can be muted.
Whereas coordinating diverse biocomputing elements to perform a computation is like herding cats, building operational circuits entirely from RNA is relatively simple and easy to scale into many different applications. All that’s needed to design ribocomputers is a careful circuit design and the ability to synthesize or express RNA in designated sequences.  With Twist Bioscience streamlining the DNA manufacturing process that can lead to RNA expression, researchers can easily go from biological circuit design to experiment, and from experiment to application.