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Stochastic modeling to explore the central dogma of molecular biology and to design more informative single-molecule, live-cell fluorescence microscopy experiments


Despite being described nearly a century ago, the Central Dogma of Molecular Biology still harbors many intricacies and mysteries that scientists have yet to unravel. With the convergence of many multidisciplinary scientific advances such as stronger computing power, next-generation sequencing, machine learning, and single-cell and single-molecule experiments, cellular biologists have never had more investigative power. These complex methods often are used in tandem--necessitating a closer relationship between computational biologists, computer scientists, and bench top experimentalists. As practice of this emerging dynamic, my corpus of work spans multiple areas within computational and quantitative biology with the goal to facilitate better computational tools to interpret and design experiment. For my main work at Colorado State University, I have developed the open source Python package "RNA sequence to Nascent protein simulator," rSNAPsim, to simulate Nascent Chain Tracking experiments and used it as a backbone for an entire experiment simulation pipeline to check experiment design feasibility. The rSNAPsim software provides start-to-finish capabilities for model design, model fitting, and model selection so that experimentalists can fit a mechanistic model to the Nascent Chain Tracking single-mRNA translation experiments. Along with this main work, I have provided computational modeling efforts on live-cell data on the first two steps of the Central Dogma, DNA transcription and mRNA translation. For the final entry in my corpus, I have used my interdisciplinary skills acquired at CSU to do machine learning based ncRNA riboswitch classification and discovery within the human genome; This work provides the broader scientific community with a starting point for searching for this important secondary structure within humans, where it has not been described as of time of writing.


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machine learning
mechanistic modeling
live-cell imaging
quantitative biology


Associated Publications