Abstract
DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.
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Acknowledgements
The authors acknowledge funding from the National Facility for Translational Medicine (Shanghai), the National Natural Science Foundation of China (grant no. 21991134), the National Key R&D Program of China (grant no. 2022YFE0100600), Microsoft Research PhD Scholarship Programme, the European Research Council (ERC project nos 101000199 AMIGA and EC-2016-674 ADG 740235, PCELLS) and the Eindhoven–Wageningen–Utrecht Alliance (https://ewuu.nl/nl/) that supports the Center for Living Technologies.
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S.Y., B.W.A.B., C.F. and T.F.A.d.G. researched data for the article. All authors contributed substantially to discussion of the content. S.Y., B.W.A.B., F.W., S.M., C.F. and T.F.A.d.G. wrote the article. All authors reviewed and/or edited the manuscript before submission.
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Glossary
- Anti-factor
-
A protein that binds sigma factor and thereby inhibits transcription.
- Base editors
-
A gene-editing approach that uses CRISPR–Cas systems to directly introduce point mutations into DNA or RNA.
- Boolean logic
-
Type of algebra that performs logical operations using truth values (TRUE or FALSE) as signals.
- Convolutional neural network
-
A neural network featuring a sparse topology, and thus low connectivity and complexity, to reduce the number of network connections and weight parameters.
- DNA circuits
-
DNA reaction networks that use the concentrations of DNA as signals to perform computational functions, mimicking electronic circuits.
- DNA classifiers
-
Molecular classifiers that use DNA as an input to distinguish different states of a DNA-encoded system.
- Feedforward loops
-
A motif of DNA circuits in which the input has two pathways (direct and indirect) to control the final output.
- Fountain codes
-
Transformation of a data file into an effectively unlimited number of encoded blocks; the original file can be reassembled by any subset of blocks.
- Hopfield neural network
-
Single-layer neural network in which all neurons are both input and output, and they are connected with equal weights.
- In vitro transcription and translation (TXTL)
-
Reactions that harness the cellular transcription and translation machinery to enable protein synthesis in vitro from natural or synthetic DNA templates.
- Near-memory computing
-
An approach in which computing units are located close to the data memory, thereby reducing data movement.
- Neural networks
-
Interconnected artificial neurons in a layered structure that resembles the biological neural network in the human brain.
- Perceptron
-
A basic building block in neural networks that computes a weighted sum of the inputs and their weights and returns a single output value corresponding to the classification of the inputs provided.
- Prime editors
-
A gene-editing approach that uses CRISPR–Cas systems to introduce changes encoded on DNA templates.
- Reed Solomon codes
-
A block coding algorithm in which a message is split into multiple message words, and the encoding algorithm is performed on each message word without any dependency on previously received message words.
- Seesaw gate
-
A gate complex with toehold domains on both sides, enabling reversible exchange of single-stranded DNA.
- Sigma factor
-
A protein required for the initiation of transcription in bacteria.
- Threshold gates
-
A gate that activates when the signal exceeds a set level.
- Winner-take-all neural network
-
Neural network in which neurons compete for activation; the output of a neuron is ON only if the weighted sum of all binary inputs is the largest among all neurons.
- XOR encoding
-
Encoding of data based on XOR logic operations.
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Yang, S., Bögels, B.W.A., Wang, F. et al. DNA as a universal chemical substrate for computing and data storage. Nat Rev Chem 8, 179–194 (2024). https://doi.org/10.1038/s41570-024-00576-4
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DOI: https://doi.org/10.1038/s41570-024-00576-4
