Abstract
Optical tweezers are routinely used to extract dynamics of nucleic acid associated motors, such as RNA polymerase (RNAP). Their ability to provide data with base-pair spatial resolution is the key. It gives us access to information encoded on the sequence-dependent mechanism underlying molecular motor processivity. Of particular interest is the statistics of RNA Polymerase's pause and dwell dependencies on DNA sequence during both the initiation and elongation stages of transcription. Inspired by results suggesting RNA Pol's DNA composition-specific transcriptional mechanism, we combine the highly temporally resolved data from optical traps with the novel statistical analysis methods of Bayesian nonparametrics (BNPs) to draw base-pair level insight on RNA Pol's activity. On account of BNPs' flexibility in recruiting models of RNA Pol dynamics warranted by the data, and RNA Pol's intrinsically complex dynamics, we rigorously constrain the models allowed by BNPs by proposing new modeling strategies inspired by BNP tools scarcely half a decade old.
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