Abstract
Predicting transcriptional dynamics of cells provides valuable insight into complex subcellular responses which lead to broad signaling pathways that dictate cellular phenotypic behavior. Understanding the transcriptional dynamics of certain genes is imperative in predicting cellular phenotypes and the behavior of cells experiencing dysregulation from environmental stresses. For this reason, we construct a computational framework that uses the output of single molecule in situ hybridization (smFISH), which supplies us with snapshots of RNA levels amongst the population of cells at different points in time, to accurately deduce the underlying gene network models.
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