UC San Diego

The recent progress of single cell quantitative microscopy produced a plethora of data that demonstrated cell to cell variability in signal transduction, which provoked many possible theories that sought to explain the phenomenon of cellular variability. In order to investigate the mechanism of cell to cell variability, mathematical modeling needs to be combined with experimental approach to gain systems-level understanding. However, until now there have been few studies which applied mathematical modeling to study single cell data because of the numerous practical and theoretical hurdles. Here I develop a computational toolbox specifically designed to perform single cell model fitting efficiently on a population scale by utilizing big data platform. The toolbox fits time series data using Bayesian parameter optimization that produces a parameter distribution for the mathematical model. The model fitting is carried out on the Google Cloud Platform which scales well with the increasing number of single cell data and allows for fitting a population of single cell data at fast pace and extreme low cost. I combine the same model fitting algorithm with single cell fluorescent microscopy experiment to study cell to cell variability in calcium signaling pathway. By analyzing the parameter distributions from single cell fitting using clustering methods, I discover distinct clusters within the parameter distributions of cell population that vary the most at IP3 receptor within the calcium signaling pathway. The theoretical findings are corroborated by subsequent experiments which demonstrated that IP3 receptors played significant role in defining calcium response identities in cells.