Abstract

Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships) is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation). Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.

Highlights

  • It is well-recognized that cellular heterogeneities exist in a population of isogenic cells [1,2,3]

  • Our results demonstrate that using the proposed framework, biologically-relevant model parameters can be attributed to individual cells and related to single-cell features, while the population of cells is represented in a concise manner

  • Gene expression in response to repeated osmotic stress shows a high level of variability between cells

Read more

Summary

Introduction

It is well-recognized that cellular heterogeneities exist in a population of isogenic cells [1,2,3]. Cellular processes are noisy and generate cell-to-cell differences. Cell-cell variability in the expression of a gene of interest can be observed over extended time scales. The origins of the variability of biological processes and phenotypes are multifarious. The observed heterogeneity of cell responses to a common stimulus is believed to originate partly from differences in cell phenotypes A proper assessment and modeling of such heterogeneity is a challenging task since it has several sources and those sources are inter-dependent and act with different strengths and on different time-scales [6]

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call