ArticleChemical science2021
A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quantum-inspired logic for advanced Transcriptional Programming.Nucleic acids research · 2025Article
- Plant intelligence dux: a comprehensive rebuttal of Kingsland and Taiz.Protoplasma · 2025Article
- Article
- Multicellular artificial neural network-type architectures demonstrate computational problem solving.Nature chemical biology · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Customizing cellular signal processing by synthetic multi-level regulatory circuits.Nature communications · 2023Review
- Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges.IEEE nanotechnology magazine · 2023Article
- Living electronics: A catalogue of engineered living electronic components.Microbial biotechnology · 2023Review
- Deep Learning Concepts and Applications for Synthetic Biology.GEN biotechnology · 2022Review
- Synthetic biology: at the crossroads of genetic engineering and human therapeutics-a Keystone Symposia report.Annals of the New York Academy of Sciences · 2021Review
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Authors and funding
4 authors.
Funding
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Abstract
Here, we adapted the basic concept of artificial neural networks (ANNs) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created cellular devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.
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Registered trials
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