基于DNA化学反应网络的最小生成树问题求解。

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Jing Yang, Yawen Zheng, Zhixiang Yin, Xianya Geng, Zhen Tang
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引用次数: 0

摘要

DNA链位移反应是一种很有前途的生物计算工具。最小生成树问题是图论中的一个基本问题。本文探讨了利用DNA链位移反应网络来解决最小生成树问题。我们还提出了一个基于DNA链位移反应的计算模型。方法:该模型通过对加权、阈值和和三个反应模块的智能集成,有效地解决了最小生成树问题。因此,最初,我们使用不同的DNA序列对图中的边进行编码,并有效地为边分配各自的权重。然后,阈值模块根据荧光强度对加权边缘进行滤波。最后,sum模块收集过滤后的边,计算最小生成树的总权值。为了验证所提方法的有效性,我们利用visual DSD软件进行了仿真实验。结果:模拟结果表明,该DNA计算模型在解决复杂问题时具有可行性和准确性。结论:本研究不仅证实了DNA计算解决图论相关问题的能力,而且为未来基于DNA的计算机系统和生物计算应用的发展提供了重要的理论支持和实验基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving Minimum Spanning Tree Problems Based on DNA Chemical Reaction Networks.

Introduction: DNA strand displacement reactions are emerging as a promising biocomputing tool. The minimum spanning tree problem is fundamental in graph theory. This paper explores the use of DNA strand displacement reaction networks for addressing the minimum spanning tree problem. We also present a computing model that is based on DNA strand displacement reactions.

Method: The model effectively solves the minimum spanning tree problem by intelligently integrating the three reaction modules of weighted, threshold, and sum. Thus, initially, we encoded the edges in the graph using distinct DNA sequences and effectively assigned the edges their respective weights. Afterwards, the threshold module applied a filter to the weighted edges based on the fluorescence intensity. Ultimately, the sum module gathered the filtered edges to calculate the overall weight of the minimum spanning tree. In order to verify the effectiveness of the proposed method, we conducted simulation experiments using visual DSD software.

Result: The results of the simulations showed the viability and precision of this DNA computing model in resolving intricate problems.

Conclusion: Furthermore, this study not only confirms the capability of DNA computing in solving problems related to graph theory, but also offers significant theoretical backing and experimental foundation for the future advancement of DNA-based computer systems and biocomputing applications.

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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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