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An interdisciplinary research team from two working groups at the Centre for Synthetic Biology at TU Darmstadt has developed the first RNA-based genetic switch that precisely replicates the logical behavior of a NAND gate, one of the most important building blocks of digital circuits. The results were published in the journal Nucleic Acids Research.
The digital RNA switches are based on so-called riboswitches: RNA sequences that can react to certain small molecules (“ligands”) (see Figure 1, left and center). As part of an mRNA, they can regulate its translation into a protein. When the ligand binds, the shape of the RNA changes, thereby creating a roadblock for the protein-producing ribosome.
Riboswitches are particularly attractive because they function without additional proteins, are very small (less than 100 nucleotides), require little energy to produce, and therefore impose only a minimal metabolic burden on the cell. This makes them ideal tools for synthetic gene regulation. Dr. Daniel Kelvin, a researcher at the Centre for Synthetic Biology at TU Darmstadt, demonstrated that seamlessly linking two riboswitches enables the creation of genetic switching elements with two different inputs.
“We use these RNA-based dual-input switches to implement logical functions in living cells, similar to those in computers. To achieve this, we have constructed a combination of two riboswitches that functions like a Boolean NAND gate.”
In digital technology, a NAND gate only outputs “off” when both inputs are ‘on’ – in all other cases, the signal remains “on.” Applied to biology, this means that gene expression is only switched off when both ligands bind to the riboswitch at the same time. If even one of the two ligands is missing, the gene remains active. Such behavior is complex and has not yet been observed in nature. In addition, the number of different sequence variants grows exponentially with sequence length. The construction of this hybrid NAND riboswitch was therefore a major challenge.
Using a combination of high-throughput laboratory screening and Bayesian optimization, a special AI method, a NAND gate was redesigned using computer models. First, a hybrid riboswitch was constructed that exhibited NAND-like behavior to some extent, thereby generating an RNA variant library. Thousands of variants of the hybrid riboswitch were produced, particularly in the central “communication module” that connects the two binding pockets of the RNA molecules. These were tested using flow cytometry and their behavior with different ligand combinations was precisely measured.
Erik Kubaczka, also a researcher at the Centre for Synthetic Biology, explains: “A deep learning model then predicts which RNA variants best fulfill the NAND function (see Figure 1 on the right). Our optimization algorithm, based on Bayesian optimization, then specifically selects new candidates – and learns with each experiment.”
It is important that the method proposes several riboswitch variants in a single step so that multiple experiments can be carried out simultaneously, thereby increasing experimental efficiency. To achieve this, the researchers used the Kriging Believer method within the otherwise sequential Bayesian optimization. Instead of waiting for the experimental data for the next suggestion after a suggestion has been made, the current model predictions are integrated into the model training. The next riboswitch variant is then selected in the context of the variants already chosen. The Kriging Believer approach ensures that sequences that are too similar are not selected, ensuring that the model can learn effectively.
After testing only 82 variants, the system found several highly optimized riboswitches. The best candidate exhibited an almost digital NAND function: a very clear separation between the “on” and “off” states.
Cells learn to make logical decisions
The development of a well-functioning NAND riboswitch is considered a milestone because all logical functions (such as AND, OR, XOR, and others) can be constructed from NAND gates.
This opens up new perspectives in living cells: cells can learn to make logical decisions – for example, to produce a product only when certain combinations of nutrients or signaling molecules are present.
In addition, biosensors for medicine and the environment can be produced that, for example, detect certain metabolic states, identify tumor signatures, or report environmental toxins only in certain combinations.
With the new hybrid riboswitch and the AI-based design approach, the team led by TU Professor Beatrix Süß (Centre for Synthetic Biology, Synthetic RNA Biology group) and Professor Heinz Koeppl (Centre for Synthetic Biology, Self-Organizing Systems group) provides a platform that significantly accelerates the construction of genetic circuits. In the future, it can be used to deploy cells even more precisely as tools in medicine, environmental technology, or industrial biotechnology.
The project impressively demonstrates how biology and artificial intelligence are converging – and how machine learning is helping to discover new functional RNA elements that nature itself has never produced.
The Centre for Synthetic Biology at TU Darmstadt
With the Centre for Synthetic Biology, synthetic biology is being established as a defined research focus at TU Darmstadt. The Centre brings together researchers from biology, chemistry, electrical and information engineering, materials science and physics, mechanical engineering, and the social sciences.
Iterative Design of a NAND Hybrid Riboswitch by Deep Batch Bayesian Optimization https://doi.org/10.1093/nar/gkag145
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