Scholarlink

<p>Due to its unique properties and excellent sequence design methods, DNA finds wide applications in computing, information storage, molecular circuits, and biological diagnosis. Previous efforts to enhance the efficiency and precision of DNA sequence design have led to the proposal of various universal DNA sequence design methods. These methods optimize the arrangement of the four bases to reduce sequence similarity and meet specific criteria. However, prior investigations have predominantly focused on sequence design within single-scene frameworks, overlooking the complexities associated with designing for multi-scene fusion, such as ion-bridge mismatch, tri-base sequence design, and others. To address this gap, we fused four common scenes and introduced two novel constraint models to facilitate DNA sequence design for multi-scene fusion. Additionally, we developed a dynamic virus spread algorithm as the core for optimizing DNA sequences and evaluated it using 23 well-known benchmark functions. Furthermore, our algorithm outperformed eight popular swarm evolutionary algorithms in eight dominant results. Finally, we simulated the optimization of four distinct scenes, demonstrating that our sequences met expected performance levels in their respective areas. Thus, our work provides a practical tool for designing DNA sequences tailored to various specific applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

DNA sequence design model for multi-scene fusion

  • 期刊论文

  • 发表日期：

    2025年1月3日

查看全文
  • Yao Yao,
  • Yanfen Zheng,
  • Shuang Cui,
  • Yaqing Hou,
  • Qiang Zhang,
  • Xiaopeng Wei

摘要

Due to its unique properties and excellent sequence design methods, DNA finds wide applications in computing, information storage, molecular circuits, and biological diagnosis. Previous efforts to enhance the efficiency and precision of DNA sequence design have led to the proposal of various universal DNA sequence design methods. These methods optimize the arrangement of the four bases to reduce sequence similarity and meet specific criteria. However, prior investigations have predominantly focused on sequence design within single-scene frameworks, overlooking the complexities associated with designing for multi-scene fusion, such as ion-bridge mismatch, tri-base sequence design, and others. To address this gap, we fused four common scenes and introduced two novel constraint models to facilitate DNA sequence design for multi-scene fusion. Additionally, we developed a dynamic virus spread algorithm as the core for optimizing DNA sequences and evaluated it using 23 well-known benchmark functions. Furthermore, our algorithm outperformed eight popular swarm evolutionary algorithms in eight dominant results. Finally, we simulated the optimization of four distinct scenes, demonstrating that our sequences met expected performance levels in their respective areas. Thus, our work provides a practical tool for designing DNA sequences tailored to various specific applications.