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Genome-scale transcriptional dynamics and environmental biosensing

Significance New technologies are needed for the global analysis of intracellular signaling networks that encode information in the time domain. We developed a microfluidic platform capable of culturing over 2,000 bacterial strains simultaneously and subjecting them to dynamical perturbation. We used an explainable artificial intelligence classifier to reveal insights embedded in the temporal transcriptional response of Escherichia coli exposed to heavy metal stress. This enabled real-time predictions of the presence of heavy metals in complex environmental samples. 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.