Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNAbased neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, a biased competitionmethod for nonlinear manipulation and analysis ofmixtures of DNA sequences was developed. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among a myriad of different DNA sequences was carried out to reduce the information entropy. The method could be used for various oligonucleotideencoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiments with DNA-encoded chemical libraries against protein targets.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89502 |
| Date | 05 March 2024 |
| Creators | Cui, Meiying, Zhao, Xueping, Reddavide, Francesco V., Patino Gaillez, Michelle, Heiden, Stephan, Mannocci, Luca, Thompson, Michael, Zhang, Yixin |
| Publisher | Oxford University Press |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 1362-4962, 10.1093/nar/gkac672 |
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