Generalized Method of Moments improves parameter estimation in biochemical signaling models of time-stamped single-cell snapshot data

Abstract

MotivationOrdinary differential equations are commonly used to model the sub-cellular dynamics of average values of proteins and mRNAs. New single-cell technologies provide cell-to-cell differences in protein/mRNA abundances that allow for the evaluation of higher order moments. However, using this additional information to improve parameter estimation can be challenging since the magnitudes of single-cell abundances can vary widely between proteins/mRNA. ResultsWe employ Generalized Method of Moments (GMM) and Particle Swarm Optimization to address the above challenges in mechanistic modeling of signaling kinetics data. Using synthetic data from linear and non-linear models, we show that the proposed method improves parameter estimation and enables construction of approximate confidence intervals. Furthermore, our approach exploits parallel computation to scale with increasing data size and dimensions. We apply our software CyGMM to estimate parameters in a linear ODE model for publicly available longitudinal single-cell cytometry data for CD8+ T cells. Our results demonstrate substantial improvements for modeling data from single-cell cytometry and RNA-seq experiments. AvailabilityWe also make freely available our estimation software CyGMM written in C++ on github (https://github.com/jhnwu3/CyGMM). Contactjayajit@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.