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DynOMICS: a total microfluidic-AI system for genome-wide E. coli transcriptional dynamics and heavy metal biosensing


Recent developments in the field of quantitative biology have demonstrated that genetic networks rely upon information encoded in their temporal dynamics, rather than beginning and ending steady-states, to govern their behavior. However, until now, there has been no tool with which to continuously observe genome-wide transcriptional dynamics without terminating the subject population. In response to this need, we developed DynOMICS, a total microfluidic and machine learning system that can monitor the state of gene expression across the E. coli genome in real time. We demonstrate its effectiveness as a field-deployable sensor, showing that it can learn the dynamic genomic signatures of heavy metal stress in both actual urban waters from several American cities and in samples from a toxic mining spill. By harnessing the microfluidics to a state-of-the-art deep neural network and an associated explanatory artificial intelligence (XAI) algorithm, we demonstrate its potential as a scientific instrument. We show that, in combination with DynOMICS, we can use deep learning networks to learn and understand bacterial transcriptional dynamics on a genome-scale. The combination of advanced microfluidics and AI-XAI is the first of its kind and is a powerful tool for quantitatively interrogating the E. coli genome.

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