Cell-To-Cell Variability in Protein Expression During Viral Infection: Monte-Carlo Simulation and Validation Based on Confocal Imaging

Abstract

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 integrated framework combining quantitative imaging and 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 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 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 captures the variability in protein expression in single cells obtained through imaging using confocal microscopy.