Birth, growth and death as structuring operators in bacterial population dynamics

Lavric, Vasile; Graham, David W.

doi:10.1016/j.jtbi.2010.01.020

Cited by 9 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulations performed by Lavric and Graham (2010) using their mathematical model suggest that all chemostats would operate under a pseudo-steady state, called confined dynamic equilibrium, which includes small-scale oscillations embedded within longer term periodic variations that have clear and finite upper and lower boundaries. A representation of these results, for living and dead cells, is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…1 Dimensionless concentrations for substrate, living and dead cells obtained solving the mathematical model applied to a chemostat; the bacteria get segregated in age classes and, consequently, experience synchronized birth and death-a whole age class die or is born, giving rise to significant changes in live and dead cells concentrations. The decrease of cells' concentration between two discrete events (birth and/or death) is due to the evacuation from the bioreactor throughout the exit line (see Lavric and Graham 2010 for a thorough discussion) Fig. 3 pO 2 and OD 600 variation for HRT=2.7 h for the chemostat operated first in batch mode and then switched to continuous.…”

Section: Resultsmentioning

confidence: 99%

“…A recent mathematical model suggests that such behaviour is theoretically feasible (Lavric and Graham 2010). The main model assumptions state that distinct cell lines result by cellular division-parent and offspring lines; each constituting a separate age class of individuals having different physical times for reproducing and dying.…”

Section: Introductionmentioning

confidence: 99%

“…A major finding from the mathematical model developed by Lavric and Graham (2010) is that bacterial populations are innately prone to synchronization according to birth and death events. This hints that cells in each age class potentially have synchronized their biological clocks and produce metabolites at the same time, triggering some quorum signals (Winzer et al 2002) in gradient-less environments (Wang et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…However, limited experimental data exist that describe birth and death cycles in bacterial populations to more broadly verify predictions of the model. To address this deficiency, we report here new experiments designed to qualitatively assess population behaviour under a broader array of operating conditions to determine the practical bounds of the mathematical model (Lavric and Graham 2010) for predicting population behaviour in bioreactors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Conditional confined oscillatory dynamics of Escherichia coli strain K12-MG1655 in chemostat systems

Ofiţeru

Ferdeş

Knapp

et al. 2011

Appl Microbiol Biotechnol

Self Cite

View full text Add to dashboard Cite

A series of continuous- and sequencing-batch reactor experiments were performed to assess the growth dynamics of Escherichia coli strain K12-MG1655 in chemostat systems. Previous mathematical predictions and early experimental results had shown that confined oscillatory dynamics ensue in bioreactor populations, which relates to "group birth and death" events within the population. New results are reported here that generally verify the predictions of the model and show that confined oscillations occur under different initial conditions, but the characteristics of the oscillatory dynamics vary as a function of the hydraulic retention time (HRT). Bioreactors were operated at HRTs ranging from 2.7 to 35 h and, regardless of initial conditions or the imposition of transient operational instabilities, highly patterned oscillations developed when HRT was between ∼3 and 8 h. However, outside of this range, bioreactor populations tended to form biofilms on the reactor walls (although the majority of the cells remained suspended in the bulk solution) and stable oscillations were not seen in the bulk phase. This suggests that alternate operating "states" might exist in chemostat populations with biofilm formation and non-homogenous spatial growth influencing "system" dynamics at very low and high HRTs. Although the model accurately predicts a confined dynamic equilibrium for mid-range HRT operations, experimental data show that model predictions do not extend outside of this range, when an alternate stable-state seems to be attained.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Conditional confined oscillatory dynamics of Escherichia coli strain K12-MG1655 in chemostat systems

Ofiţeru

Ferdeş

Knapp

et al. 2011

Appl Microbiol Biotechnol

Self Cite

View full text Add to dashboard Cite

show abstract

From Single Cells to Microbial Population Dynamics: Modelling in Biotechnology Based on Measurements of Individual Cells

Bley

2010

High Resolution Microbial Single Cell Analytics

View full text Add to dashboard Cite

The development of dynamic modelling of microbial populations in bioprocesses is reviewed. In the 1960s Arnold Fredrickson established the theoretical basis of such models, and other researchers have subsequently advanced them. This review explores the relationships that describe cell proliferation and evaluates the importance of the application of flow cytometry to the fundamental parameterisation of the models for their use in bioprocess engineering. The section "Individual-Based Modelling" discusses recent theoretical developments. Delay-differential equations are demonstrated to describe accurately temporal variation of the cell proliferation cycle and specialised approaches and related iconography are applied to stochastic and deterministic modelling of stages of cellular development. Synchronised cultures of the bacterium Cupriavidus necator were prepared and monitored using a flow cytometer. The data obtained demonstrate that cell proliferation could be simulated quantitatively using the developed model.

show abstract

Non-linear population dynamics in chemostats associated with live–dead cell cycling in Escherichia coli strain K12-MG1655

Fru

Ofiţeru

Lavric

et al. 2010

Appl Microbiol Biotechnol

View full text Add to dashboard Cite

Bacterial populations conditionally display non-linear dynamic behaviour in bioreactors with steady inputs, which is often attributed to varying habitat conditions or shifting intracellular metabolic activity. However, mathematical modelling has predicted that such dynamics also might simply result from staggered birth, growth, and death events of groups of cells within the population, causing density oscillations and the cycling of live and dead cells within the system. To assess this prediction, laboratory experiments were performed on Escherichia coli strain K12-MG1655 grown in chemostats to first define fine-scale population dynamics over time (minutes) and then determine whether the dynamics correlate with live-dead cell cycles in the system. E. coli populations displayed consistent oscillatory behaviour in all experiments. However, close synchronisation between OD₆₀₀ and live-dead cell oscillations (within ~33-38 min cycles) only became statistically significant (p < 0.01) when pseudo-steady state operations approaching carrying capacity existed in the bioreactor. Specifically, live cells were highest at local OD₆₀₀ maxima and lowest at local OD₆₀₀ minima, showing that oscillations followed live-dead cell cycles as predicted by the model and also consistent with recent observations that death is non-stochastic in such populations. These data show that oscillatory dynamic behaviour is intrinsic in bioreactor populations, which has implications to process operations in biotechnology.

show abstract

Birth, growth and death as structuring operators in bacterial population dynamics

Cited by 9 publications

References 32 publications

Conditional confined oscillatory dynamics of Escherichia coli strain K12-MG1655 in chemostat systems

Conditional confined oscillatory dynamics of Escherichia coli strain K12-MG1655 in chemostat systems

From Single Cells to Microbial Population Dynamics: Modelling in Biotechnology Based on Measurements of Individual Cells

Non-linear population dynamics in chemostats associated with live–dead cell cycling in Escherichia coli strain K12-MG1655

Contact Info

Product

Resources

About