Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of these networks is that cells use many layers of regulatory feedback to constantly assess and precisely react to their environment. The importance of complex feedback in controlling the real-time response to external stimuli has led to a need for the next generation of cell-based technologies that enable both the collection and analysis of high-throughput temporal data. Toward this end, we have developed a microfluidic platform capable of monitoring temporal gene expression from over 2,000 promoters. By coupling the "Dynomics" platform with deep neural network (DNN) and associated explainable artificial intelligence (XAI) algorithms, we show how machine learning can be harnessed to assess patterns in transcriptional data on a genome scale and identify which genes contribute to these patterns. Furthermore, we demonstrate the utility of the Dynomics platform as a field-deployable real-time biosensor through prediction of the presence of heavy metals in urban water and mine spill samples, based on the the dynamic transcription profiles of 1,807 unique Escherichia coli promoters.

中文翻译：

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基因组规模转录动力学和环境生物传感。

基因组规模的技术已经能够绘制控制细胞行为的复杂分子网络。这些网络分析中的一个新兴主题是，细胞使用多层调节反馈来不断评估环境并对其环境做出精确反应。复杂反馈在控制对外部刺激的实时响应方面的重要性导致了对下一代基于细胞的技术的需求，这些技术能够收集和分析高通量时态数据。为此，我们开发了一个微流体平台，能够监测 2,000 多个启动子的时间基因表达。通过将“Dynamics”平台与深度神经网络（DNN）和相关的可解释人工智能（XAI）算法相结合，我们展示了如何利用机器学习来评估基因组规模的转录数据模式，并识别哪些基因对这些模式有贡献。此外，我们基于 1,807 个独特的大肠杆菌启动子的动态转录谱，通过预测城市水和矿井泄漏样品中重金属的存在，展示了 Dynomics 平台作为可现场部署的实时生物传感器的实用性。