Winner(s)-take-all: nonlinear amplification of DNA-encoded library

Information processing functions are essential for biological organisms to perceive and react to their complex enviornment, as well as for human to analyze and rationalize them. While our brain has an extraordinary power to process complex information, winner(s)-take-all computation is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNA-based 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, we developed a winner(s)-take-all method for non-linear amplification of mixtures of DNA sequences. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among myriad of different DNA sequences was carried out with a neural network-inspired winner-take-all function, to reduce the information complexity. The method could be used for various oligonucleotide-encoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiment with DNA-encoded chemical library against protein target.