{"title":"工程细菌利用多细胞人工神经网络结构将3位二进制码转换为3位灰色码。","authors":"Saswata Chakraborty, Sangram Bagh","doi":"10.1021/acssynbio.5c00145","DOIUrl":null,"url":null,"abstract":"<p><p>The neuromorphic computing with genetically engineered cells is still in its infancy and shows great promise to solve various complex computational problems. The success of such computing is dependent on the expansion of its capability to build new and versatile computation functions. The conversion of a binary code to a Gray code is a fundamental concept in digital electronics and computer science. In this work, by using genetically engineered <i>E. coli</i> cells, we created a single-layer artificial neural network (ANN) that works as a 3-bit-binary to Gray code converter. The ANN architecture is built by five engineered <i>E. coli</i> populations in a liquid culture, where a binary input in chemical form is given by adding or not adding (1/0) three chemical inputs, and the converted codes are manifested by the appropriate expression of three fluorescent proteins. The work may have significance in biocomputer technology development, bacterial ANN, and synthetic biology.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Engineered Bacteria Convert a 3-Bit Binary Code to a 3-Bit Gray Code by Multicellular Artificial-Neural-Network-Type Architecture.\",\"authors\":\"Saswata Chakraborty, Sangram Bagh\",\"doi\":\"10.1021/acssynbio.5c00145\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The neuromorphic computing with genetically engineered cells is still in its infancy and shows great promise to solve various complex computational problems. The success of such computing is dependent on the expansion of its capability to build new and versatile computation functions. The conversion of a binary code to a Gray code is a fundamental concept in digital electronics and computer science. In this work, by using genetically engineered <i>E. coli</i> cells, we created a single-layer artificial neural network (ANN) that works as a 3-bit-binary to Gray code converter. The ANN architecture is built by five engineered <i>E. coli</i> populations in a liquid culture, where a binary input in chemical form is given by adding or not adding (1/0) three chemical inputs, and the converted codes are manifested by the appropriate expression of three fluorescent proteins. The work may have significance in biocomputer technology development, bacterial ANN, and synthetic biology.</p>\",\"PeriodicalId\":26,\"journal\":{\"name\":\"ACS Synthetic Biology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Synthetic Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1021/acssynbio.5c00145\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Synthetic Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1021/acssynbio.5c00145","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Engineered Bacteria Convert a 3-Bit Binary Code to a 3-Bit Gray Code by Multicellular Artificial-Neural-Network-Type Architecture.
The neuromorphic computing with genetically engineered cells is still in its infancy and shows great promise to solve various complex computational problems. The success of such computing is dependent on the expansion of its capability to build new and versatile computation functions. The conversion of a binary code to a Gray code is a fundamental concept in digital electronics and computer science. In this work, by using genetically engineered E. coli cells, we created a single-layer artificial neural network (ANN) that works as a 3-bit-binary to Gray code converter. The ANN architecture is built by five engineered E. coli populations in a liquid culture, where a binary input in chemical form is given by adding or not adding (1/0) three chemical inputs, and the converted codes are manifested by the appropriate expression of three fluorescent proteins. The work may have significance in biocomputer technology development, bacterial ANN, and synthetic biology.
期刊介绍:
The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism.
Topics may include, but are not limited to:
Design and optimization of genetic systems
Genetic circuit design and their principles for their organization into programs
Computational methods to aid the design of genetic systems
Experimental methods to quantify genetic parts, circuits, and metabolic fluxes
Genetic parts libraries: their creation, analysis, and ontological representation
Protein engineering including computational design
Metabolic engineering and cellular manufacturing, including biomass conversion
Natural product access, engineering, and production
Creative and innovative applications of cellular programming
Medical applications, tissue engineering, and the programming of therapeutic cells
Minimal cell design and construction
Genomics and genome replacement strategies
Viral engineering
Automated and robotic assembly platforms for synthetic biology
DNA synthesis methodologies
Metagenomics and synthetic metagenomic analysis
Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction
Gene optimization
Methods for genome-scale measurements of transcription and metabolomics
Systems biology and methods to integrate multiple data sources
in vitro and cell-free synthetic biology and molecular programming
Nucleic acid engineering.