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Single-cell variation leads to population invariance in NF-?B signaling dynamics.

ABSTRACT: The activation dynamics of nuclear factor (NF)-?B have been shown to affect downstream gene expression. On activation, NF-?B shuttles back and forth across the nuclear envelope. Many dynamic features of this shuttling have been characterized, and most features vary significantly with respect to ligand type and concentration. Here, we report an invariant feature with regard to NF-?B dynamics in cellular populations: the distribution--the average, as well as the variance--of the time between two nuclear entries (the period). We find that this period is conserved, regardless of concentration and across several different ligands. Intriguingly, the distributions observed at the population level are not observed in individual cells over 20-h time courses. Instead, the average period of NF-?B nuclear translocation varies considerably among single cells, and the variance is much smaller within a cell than that of the population. Finally, analysis of daughter-cell pairs and isogenic populations indicates that the dynamics of the NF-?B response is heritable but diverges over multiple divisions, on the time scale of weeks to months. These observations are contrary to the existing theory of NF-?B dynamics and suggest an additional level of control that regulates the overall distribution of translocation timing at the population level.

SUBMITTER: Hughey JJ

PROVIDER: S-EPMC4310747 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Single-cell variation leads to population invariance in NF-κB signaling dynamics.

Hughey Jacob J JJ Gutschow Miriam V MV Bajar Bryce T BT Covert Markus W MW

Molecular biology of the cell 20141203 3

The activation dynamics of nuclear factor (NF)-κB have been shown to affect downstream gene expression. On activation, NF-κB shuttles back and forth across the nuclear envelope. Many dynamic features of this shuttling have been characterized, and most features vary significantly with respect to ligand type and concentration. Here, we report an invariant feature with regard to NF-κB dynamics in cellular populations: the distribution--the average, as well as the variance--of the time between two n ...[more]

PMID: 25473117

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Project description:A few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua, and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

Dataset Information

Single-cell variation leads to population invariance in NF-?B signaling dynamics.

Publications

Single-cell variation leads to population invariance in NF-κB signaling dynamics.

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