Scholarlink

<p>Engineering genetic circuits to process complex biological signals remains a significant challenge due to non-orthogonal signal responses that limit precise control. In this study, we introduce a framework that integrates orthogonal operational amplifiers (OAs) into standardized biological processes to enable efficient signal decomposition and amplification. By engineering σ/anti-σ pairs, varying ribosome binding site (RBS) strengths, and utilizing both open-loop and closed-loop configurations, we design scalable OAs that enhance the precision, adaptability, and signal-to-noise ratio of genetic circuits. Additionally, we present a prototype whole-cell biosensor capable of detecting transcriptional changes in response to growth conditions, enabling growth-state-responsive induction systems. These systems provide dynamic gene expression control without external inducers, offering significant advantages for metabolic engineering applications. We also apply our framework to mitigate crosstalk in multi-signal systems, ensuring independent control over each signal channel within complex biological networks. Our approach enhances synthetic biology systems by robust signal processing and precise dynamic regulation.</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

A framework for complex signal processing via synthetic biological operational amplifiers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月31日

查看全文
  • Wenjun Cao,
  • Lili Liu,
  • Qingxu Sun,
  • Yang Shan,
  • Ye Chen

摘要

Engineering genetic circuits to process complex biological signals remains a significant challenge due to non-orthogonal signal responses that limit precise control. In this study, we introduce a framework that integrates orthogonal operational amplifiers (OAs) into standardized biological processes to enable efficient signal decomposition and amplification. By engineering σ/anti-σ pairs, varying ribosome binding site (RBS) strengths, and utilizing both open-loop and closed-loop configurations, we design scalable OAs that enhance the precision, adaptability, and signal-to-noise ratio of genetic circuits. Additionally, we present a prototype whole-cell biosensor capable of detecting transcriptional changes in response to growth conditions, enabling growth-state-responsive induction systems. These systems provide dynamic gene expression control without external inducers, offering significant advantages for metabolic engineering applications. We also apply our framework to mitigate crosstalk in multi-signal systems, ensuring independent control over each signal channel within complex biological networks. Our approach enhances synthetic biology systems by robust signal processing and precise dynamic regulation.