Cell-to-Cell Variability in Protein Expression during Viral Infection: Monte-Carlo Simulation and Validation based on Confocal Imaging.

One of the major challenges is to identify the statistical model underlying the heterogeneity in viral protein expression in single cells. In this endeavor, we propose a computational tool to address the cell-to-cell variability in protein expression by random variate generation following probability distributions. Here, we show that statistical modeling using the probability density function of various distribution offers considerable potential for providing stochastic inputs to Monte Carlo simulation. Specifically, we present the ranking between three distribution families including gamma, normal and Weibull distribution using a comparison of cumulative frequency obtained from experiment and simulation. The major contribution of the proposed simulation method is to identify the underlying statistical model in kinetic parameters that capture the variability in protein expression in single cells obtained through imaging using confocal microscopy.