Connecting Noisy Single-Cell Dynamics to Smooth Population Growth

Physics ◽  
2018 ◽  
Vol 11 ◽  
Author(s):  
Anonymous
Keyword(s):  
Population Growth ◽  
Single Cell ◽  
Cell Dynamics

scholarly journals Noise-driven growth rate gain in clonal cellular populations

2016 ◽  
Vol 113 (12) ◽  
pp. 3251-3256 ◽  
Author(s):  
Mikihiro Hashimoto ◽  
Takashi Nozoe ◽  
Hidenori Nakaoka ◽  
Reiko Okura ◽  
Sayo Akiyoshi ◽  
...  
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Single Cell ◽  
Single Cells ◽  
Population Level ◽  
Growth Conditions ◽  
Cell Dynamics ◽  
Generation Times ◽  
Complex Population ◽  
The Mean

Cellular populations in both nature and the laboratory are composed of phenotypically heterogeneous individuals that compete with each other resulting in complex population dynamics. Predicting population growth characteristics based on knowledge of heterogeneous single-cell dynamics remains challenging. By observing groups of cells for hundreds of generations at single-cell resolution, we reveal that growth noise causes clonal populations of Escherichia coli to double faster than the mean doubling time of their constituent single cells across a broad set of balanced-growth conditions. We show that the population-level growth rate gain as well as age structures of populations and of cell lineages in competition are predictable. Furthermore, we theoretically reveal that the growth rate gain can be linked with the relative entropy of lineage generation time distributions. Unexpectedly, we find an empirical linear relation between the means and the variances of generation times across conditions, which provides a general constraint on maximal growth rates. Together, these results demonstrate a fundamental benefit of noise for population growth, and identify a growth law that sets a “speed limit” for proliferation.

scholarly journals Modeling Bacterial Population Growth from Stochastic Single-Cell Dynamics

2014 ◽  
Vol 80 (17) ◽  
pp. 5241-5253 ◽  
Author(s):  
Antonio A. Alonso ◽  
Ignacio Molina ◽  
Constantinos Theodoropoulos
Keyword(s):  
Differential Equation ◽  
Population Growth ◽  
Single Cell ◽  
Bacterial Cells ◽  
Estimation Of Parameters ◽  
Deterministic Models ◽  
Cell Dynamics ◽  
Bacterial Populations ◽  
Content Type ◽  
Number Of Individuals

ABSTRACTA 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 forEscherichia coli,Listeria innocua, andSalmonella 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.

Single-cell dynamics of T-cell priming

2007 ◽  
Vol 19 (3) ◽  
pp. 249-258 ◽  
Author(s):  
S HENRICKSON ◽  
U VONANDRIAN
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Cell Dynamics ◽  
T Cell Priming

scholarly journals Stem cell dynamics in mouse hair follicles: A story from cell division counting and single cell lineage tracing

Cell Cycle ◽  
2010 ◽  
Vol 9 (8) ◽  
pp. 1504-1510 ◽  
Author(s):  
Ying V. Zhang ◽  
Brian S. White ◽  
David I. Shalloway ◽  
Tudorita Tumbar
Keyword(s):  
Stem Cell ◽  
Cell Division ◽  
Single Cell ◽  
Cell Lineage ◽  
Hair Follicles ◽  
Lineage Tracing ◽  
Cell Dynamics

Two-State Membrane Potential Fluctuations Driven by Weak Pairwise Correlations

2004 ◽  
Vol 16 (11) ◽  
pp. 2351-2378 ◽  
Author(s):  
Andrea Benucci ◽  
Paul F.M.J. Verschure ◽  
Peter König
Keyword(s):  
Membrane Potential ◽  
Single Cell ◽  
Cortical Neurons ◽  
Bimodal Distribution ◽  
Quantitative Agreement ◽  
Neuronal Model ◽  
Dendritic Morphology ◽  
Cell Dynamics ◽  
Up States ◽  
Pairwise Correlations

Physiological experiments demonstrate the existence of weak pairwise correlations of neuronal activity in mammalian cortex (Singer, 1993). The functional implications of this correlated activity are hotly debated (Roskiesetal., 1999).Nevertheless, it is generally considered a wide spread feature of cortical dynamics. In recent years, another line of research has attracted great interest: the observation of a bimodal distribution of the membrane potential defining up states and down states at the single cell level (Wilson & Kawaguchi, 1996; Steriade, Contreras, & Amzica, 1994; Contreras & Steriade, 1995; Steriade, 2001). Here we use a theoretical approach to demonstrate that the latter phenomenon is a natural consequence of the former. In particular, we show that weak pairwise correlations of the inputs to a compartmental model of a layer V pyramidal cell can induce bimodality in its membrane potential. We show how this relationship can account for the observed increase of the power in the γ frequency band during up states, as well as the increase in the standard deviation and fraction of time spent in the depolarized state (Anderson, Lampl, Reichova, Carandini, & Ferster, 2000). In order to quantify the relationship between the correlation properties of a cortical network and the bistable dynamics of single neurons, we introduce a number of new indices. Subsequently, we demonstrate that a quantitative agreement with the experimental data can be achieved, introducing voltage-dependent mechanisms in our neuronal model such as Ca2+- and Ca2+-dependent K+ channels. In addition, we show that the up states and down states of the membrane potential are dependent on the dendritic morphology of cortical neurons. Furthermore, bringing together network and single cell dynamics under a unified view allows the direct transfer of results obtained in one context to the other and suggests a new experimental paradigm: the use of specific intracellular analysis as a powerful tool to reveal the properties of the correlation structure present in the network dynamics.

scholarly journals In Vivo Biochemistry: Single-Cell Dynamics of Cyclic Di-GMP in Escherichia coli in Response to Zinc Overload

Biochemistry ◽  
2017 ◽  
Vol 57 (1) ◽  
pp. 108-116 ◽  
Author(s):  
Jongchan Yeo ◽  
Andrew B. Dippel ◽  
Xin C. Wang ◽  
Ming C. Hammond
Keyword(s):  
Escherichia Coli ◽  
Single Cell ◽  
Cell Dynamics

scholarly journals Single-cell dynamics of the chromosome replication and cell division cycles in mycobacteria

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
Isabella Santi ◽  
Neeraj Dhar ◽  
Djenet Bousbaine ◽  
Yuichi Wakamoto ◽  
John D. McKinney
Keyword(s):  
Cell Division ◽  
Single Cell ◽  
Chromosome Replication ◽  
Cell Dynamics
Sign in / Sign up

Export Citation Format

Share Document