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The ability to measure the full complement of RNA molecules in space and time within individual cells—and to interpret the torrent of data emerging from these technologies—is transforming how we dissect biological function. In this series of hands-on lectures, we will explore how physical modeling can engage in a dialogue with these new data modalities to uncover the molecular mechanisms governing transcription and its regulation. Participants will be introduced to the basics of dynamical systems theory and chemical master equations to predict both the mean and distribution of mRNA over time. We will also develop a “theory of the experiment” for single-cell sequencing, examining how to extract meaningful mechanistic insights from complex datasets. The sessions will combine pen-and-paper derivations with simple coding exercises in Google Colab, accessible from any laptop or tablet. No prior background in mathematics, physics, or programming is required.

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