Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning

Clicks: 144
ID: 270174
2019
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Abstract
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.
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baek2019moleculesenzymatic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Christina Baek;Sang-Woo Lee;Beom-Jin Lee;Dong-Hyun Kwak;Byoung-Tak Zhang;Baek, Christina;Lee, Sang-Woo;Lee, Beom-Jin;Kwak, Dong-Hyun;Zhang, Byoung-Tak;
Journal molecules
Year 2019
DOI
10.3390/molecules24071409
URL
https://www.mdpi.com/1420-3049/24/7/1409
Keywords
Machine learning pattern classification dna computing molecular computing molecular learning self-organizing systems

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