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Cyberbiosecurity:Advancements in DNA-based information security
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作者 tuoyu liu Sijie Zhou +1 位作者 Tao Wang Yue Teng 《Biosafety and Health》 CAS CSCD 2024年第4期251-256,共6页
Synthetic biology is a crucial component of the“cyber‐biological revolution”in this new industrial revolution.Owing to breakthroughs in synthetic biology,deoxyribonucleic acid(DNA),the storehouse of hereditary mate... Synthetic biology is a crucial component of the“cyber‐biological revolution”in this new industrial revolution.Owing to breakthroughs in synthetic biology,deoxyribonucleic acid(DNA),the storehouse of hereditary material in biological systems,can now be used as a medium for storage(synthesis)and reading(sequencing)of information.However,integrating synthetic biology with computerization has also caused cyberbiosecurity concerns,encompassing biosecurity and information security issues.Malicious codes intended to attack computer systems can be stored as artificially synthesized DNA fragments,which can be released during DNA sequencing and decoding and attack computer and network systems.As these cyberbiosecurity threats become increasingly realistic,spreading awareness and information about how they can be prevented and controlled is crucial.This review aims to address this need by offering crucial theoretical backing for cyberbiosecurity research and raising awareness of risk mitigation and control measures in information security,biosecurity,and national security。 展开更多
关键词 Synthetic biology Cyberbiosecurity Information security Deoxyribonucleic acid(DNA)storage Deoxyribonucleic acid(DNA)sequencing
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SemiSynBio:A new era for neuromorphic computing
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作者 Ruicun liu tuoyu liu +6 位作者 Wuge liu Boyu Luo Yuchen Li Xinyue Fan Xianchao Zhang Wei Cui Yue Teng 《Synthetic and Systems Biotechnology》 SCIE CSCD 2024年第3期594-599,共6页
Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing h... Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence(AI)systems,due to its advantages of adaptive learning and parallel computing.Meanwhile,biocomputing has seen ongoing development with the rise of synthetic biology,becoming the driving force for new generation semiconductor synthetic biology(SemiSynBio)technologies.DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks(ANNs),providing the possibility of executing neuromorphic computing at the molecular level.Herein,we briefly outline the principles of neuromorphic computing,describe the advances in DNA computing with a focus on synthetic neuromorphic computing,and summarize the major challenges and prospects for synthetic neuromorphic computing.We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing,which would be of widespread use in biocomputing,DNA storage,information security,and national defense. 展开更多
关键词 Neuromorphic computing Synthetic biology BIOCOMPUTING Artificial intelligence Neuromorphic genetic circuits
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MECE:基于深度神经网络及进化分析提高糖苷水解酶的催化效率 被引量:1
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作者 刘汗青 关菲菲 +8 位作者 刘拓宇 杨丽鑫 范灵熙 刘晓青 罗会颖 伍宁丰 姚斌 田健 黄火清 《Science Bulletin》 SCIE EI CAS CSCD 2023年第22期2793-2805,M0006,共14页
糖苷水解酶(glycoside hydrolases,GHs)在各个行业广泛应用,其需求量不断增加.然而,如何提高酶的催化效率仍然是一个挑战.本文开发了基于深度神经网络和分子进化的策略(MECE)预测提高糖苷水解酶催化活性的突变体.作者首先从CAZy数据库... 糖苷水解酶(glycoside hydrolases,GHs)在各个行业广泛应用,其需求量不断增加.然而,如何提高酶的催化效率仍然是一个挑战.本文开发了基于深度神经网络和分子进化的策略(MECE)预测提高糖苷水解酶催化活性的突变体.作者首先从CAZy数据库中收集整理了119个糖苷水解酶家族的蛋白序列,建立了能够识别糖苷水解酶家族和功能残基的深度学习模型DeepGH,通过10倍交叉验证结果显示DeepGH模型的预测准确率为96.73%.随后利用梯度加权类激活图谱(Grad-CAM)方法提取分类相关特征,结合序列进化信息对突变体进行设计最后获得了具有7个氨基酸突变位点的壳聚糖酶突变体CHIS1754-MUT7.实验结果表明,CHIS1754-MUT7的k_(cat)/K_m是野生型的23.53倍.该策略计算效率高,实验成本低,具有显著的优势,为酶催化效率的智能设计提供了一种新的途径,具有广泛的应用前景. 展开更多
关键词 MECE Deep learning Catalytic efficiency Glycoside hydrolases Feature extraction
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SMOC:a smart model for open chromatin region prediction in rice genomes 被引量:3
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作者 Weijun Guo Hanqing liu +8 位作者 Yifan Wang Pingxian Zhang Dongwei Li tuoyu liu Qian Zhang Liwen Yang Li Pu Jian Tian Xiaofeng Gu 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2022年第5期514-517,共4页
In eukaryotic genomes, the fundamental unit of chromatin is the nucleosome, which is composed of a histone octamer that contains two copies of each histone, H3, H2A, H2B, and H4 (Bell et al., 2011).Chromatin is wrappe... In eukaryotic genomes, the fundamental unit of chromatin is the nucleosome, which is composed of a histone octamer that contains two copies of each histone, H3, H2A, H2B, and H4 (Bell et al., 2011).Chromatin is wrapped in the nucleus in a highly helical state to play an essential role in gene regulation (Li et al., 2007). 展开更多
关键词 SMART PREDICTION HELICAL
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Nanoscale storage encryption: data storage in synthetic DNA using a cryptosystem with a neural network 被引量:3
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作者 Yue Teng Shan Yang +8 位作者 Liyan liu Ruicun liu Yaofeng Chen Jinyu Li Qian Yang tuoyu liu Yujun Cui Peng Cheng Shengqi Wang 《Science China(Life Sciences)》 SCIE CAS CSCD 2022年第8期1673-1676,共4页
Dear Editor,Synthetic DNA is emerging as a new data storage medium with high density and durable preservation capability potential(Ceze et al., 2019;Yazdi et al., 2015), thus enhancing the security of data stored in D... Dear Editor,Synthetic DNA is emerging as a new data storage medium with high density and durable preservation capability potential(Ceze et al., 2019;Yazdi et al., 2015), thus enhancing the security of data stored in DNA is particularly important(Cherian et al., 2013;El-Zoghabi et al., 2013;Kalsi et al.,2018). 展开更多
关键词 NEURAL network SYNTHETIC
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利用基因线路构建神经网络实现神经拟态计算 被引量:2
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作者 杨姗 刘芮存 +3 位作者 刘拓宇 庄滢潭 李金玉 滕越 《科学通报》 EI CAS CSCD 北大核心 2021年第31期3992-4002,共11页
随着大数据时代的到来,现有计算体系限制了人工智能与类脑计算等新技术的发展.基于人工神经网络的神经拟态计算则提供了潜在解决方案,而具备低能耗并行化等优势的生物计算对其研究至关重要,其中基因线路将是构造人工神经网络的关键.本... 随着大数据时代的到来,现有计算体系限制了人工智能与类脑计算等新技术的发展.基于人工神经网络的神经拟态计算则提供了潜在解决方案,而具备低能耗并行化等优势的生物计算对其研究至关重要,其中基因线路将是构造人工神经网络的关键.本研究利用生物元件构建设计了人工神经网络,并实现了线性分类、非线性分类以及图案分类等神经拟态计算应用,充分利用生物元件的特性,模拟神经网络中神经元的连接关系,根据生物元件在基因线路中产生不同响应作为元件选择标准,构建基因线路实现不同功能的神经拟态计算.本研究提出通过工程化基因线路构建人工神经网络实现神经拟态计算,由此集成的分子计算系统有望应用于人工智能芯片的制造,并进一步广泛应用于类脑计算、脑机接口及国防建设等领域. 展开更多
关键词 神经拟态计算 人工神经网络 DNA计算 基因线路 合成生物学
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