Please use this identifier to cite or link to this item: https://repository.usc.edu.co/handle/20.500.12421/247
Title: What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast
Authors: Llamosi, Artémis
González-Vargas, Andrés M.
Versari, Cristian
Cinquemani, Eugenio
Ferrari-Trecate, Giancarlo
Hersen, Pascal
Batt, Grégory
Keywords: Computational Biology
Gene Expression
Microfluidic Analytical Techniques
Models, Biological
Saccharomyces cerevisiae
Single-Cell Analysis
Issue Date: 9-Feb-2016
Publisher: Public Library of Science
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. © 2016 Llamosi et al.
URI: https://repository.usc.edu.co/handle/20.500.12421/247
ISSN: 1553734X
Appears in Collections:Artículos Científicos



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