应用评估和以性能为导向的本地和工程生物传感器的改进
Min Li1, Zhenya Chen1,2, Yi-Xin Huo1,2
1Department of Gastroenterology, Aerospace Center Hospital, College of Life Science, Beijing Institute of Technology, Haidian District, No. 5 South Zhongguancun Street, Beijing 100081, China.
ACS sensors
|October 11, 2024
概括
基于转录因子 (TF) 的生物传感器 (TFB) 提供快速检测,但本地设计存在局限性. 本综述详细介绍了为各种应用,包括人工智能集成设计高效TFB的策略.
科学领域:
- 合成生物学 合成生物学
- 生物传感器工程的工程.
- 分子生物学分子生物学
背景情况:
- 基于转录因子 (TF) 的生物传感器 (TFB) 将生物信号转化为可测量的输出,为传统检测方法提供了替代方案.
- 原生TFBs经常显示低于最佳的性能 (低特异性,灵敏度,狭窄的动态范围) 由于微生物生存的进化优化,而不是实验室使用.
研究的目的:
- 分析TFB的监管机制,并提出构建高效传感系统的策略.
- 审查最近的进展,商业化方面的挑战和TFB的系统改进.
- 提出未来的方向,包括用于增强TFB应用的AI驱动编程.
主要方法:
- 对四个管辖TFB的监管机制的分析.
- 审查TFB技术的最新进展和商业化挑战.
- 讨论TFB性能提升的元素修改策略.
主要成果:
- 确定了原生TFB的局限性,并概述了改进性能的工程策略.
- 突出了各个领域TFB应用的进展.
- 拟议的未来研究途径侧重于快速响应,应用隔离和AI集成.
结论:
- 工程TFB对于克服局限性和扩大其实用性至关重要.
- 人工智能和先进的遗传电路编程对下一代TFB具有重大潜力.
- 优化的TFB可以推动工业4.0应用的进步,例如生物制造和诊断.
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