On the Distinction Between Lag and Delay in Population Growth (original) (raw)

Mathematical modeling of lag phases in microbial growth

Biotechnology and Bioengineering, 1978

This paper describes a mathematical model of the lag phases of Sacdzarornycc~s cerrvisiae that incorporates the basic concepts previously presented in a two-stage deterministic model for the growth of this organism under conditions of oxygen excess with a sugar as the growth-limiting substrate. The model structure was suggested by an extensive investigation of the causes of the lag phases of S. cerevisiae which found that, in contrast to the traditionally accepted trends, the length of the lag phase was not inoculum-size dependent. This was consistent with other previously published work which suggested that a major factor in the length of the lag phases in S. cerevisiae was the need to synthesize adequate levels of glycolytic and respiratory enzymes. These suggestions were confirmed experimentally with lag-age data. Based on this conclusion a mathematical model was developed incorporating a description of the levels of glycolytic and respiratory enzymes and their effect on the growth rate and metabolism. This model was tested experimentally and the initial results indicate that many aspects of the lag phase of this organism may be described mathematically. The experimental findings further support the concept of primary regulatory control proposed by Bijkerk and Hall.

Analysis and IbM simulation of the stages in bacterial lag phase: Basis for an updated definition

Journal of Theoretical Biology, 2008

The lag phase is the initial phase of a culture that precedes exponential growth and occurs when the conditions of the culture medium differ from the pre-inoculation conditions. It is usually defined by means of cell density because the number of individuals remains approximately constant or slowly increases, and it is quantified with the lag parameter l. The lag phase has been studied through mathematical modelling and by means of specific experiments. In recent years, Individual-based Modelling (IbM) has provided helpful insights into lag phase studies.

Analysis of the lag phase to exponential growth transition by incorporating inoculum characteristics

Food Microbiol, 2011

During the last decade, individual-based modelling (IbM) has proven to be a valuable tool for modelling and studying microbial dynamics. As each individual is considered as an independent entity with its own characteristics, IbM enables the study of microbial dynamics and the inherent variability and heterogeneity. IbM simulations and (single-cell) experimental research form the basis to unravel individual cell characteristics underlying population dynamics. In this study, the IbM framework MICRODIMS, i.e., MICRObial Dynamics Individual-based Model/Simulator, is used to investigate the system dynamics (with respect to the model and the system modelled). First, the impact of the time resolution on the simulation accuracy is discussed. Second, the effect of the inoculum state and size on emerging individual dynamics, such as individual mass, individual age and individual generation time distribution dynamics, is studied. The distributions of individual characteristics are more informative during the lag phase and the transition to the exponential growth phase than during the exponential phase. The first generation time distributions are strongly influenced by the inoculum state. All inocula with a pronounced heterogeneity, except the inocula starting from a uniform distribution, exhibit commonly observed microbial behaviour, like a more spread first generation time distribution compared to following generations and a fast stabilisation of biomass and age distributions.

Cell division theory and individual-based modeling of microbial lag

International Journal of Food Microbiology, 2005

This paper is the second in a series of two, and studies microbial lag in cell number and/or biomass measurements caused by temperature changes with an individual-based modeling approach. For this purpose, the theory of cell division, as discussed in the first part of this series of research papers, was implemented in the individual-based modeling framework BacSim. Simulations of this model are compared with experimental data of Escherichia coli, growing in an aerated, glucose-rich medium and subjected to sudden temperature shifts. The premise of a constant cell volume under changing temperature conditions predicts no lag in cell numbers after the shift, in contrast to the experimental observations. Based on literature research, two biological mechanisms that could be responsible for the observed lag phenomena are proposed. The first assumes that the average cell volume depends on temperature while the second assumes that a lag in biomass growth occurs after the temperature shift. For a lag in cell number caused by an increased average cell volume, the cell biomass always increases at the maximal rate. Therefore, cells are evidently not stressed and do not have to adapt to the new conditions, as opposed to a lag in biomass growth. Implementation and simulation of both mechanisms are found to describe the experimental observations equally well. Therefore, further research is needed to distinguish between the two mechanisms. This can be done by observing, in addition to cell numbers, a measure for the average cell volumes. In conclusion, the individual-based modeling approach is a good methodology to investigate and test biological theories and assumptions. Also, based on the simulations, suggestions for further experimental observations can be made. D

Modeling delayed processes in biological systems

Physical Review E, 2016

Delayed processes are ubiquitous in biological systems and are often characterized by delay differential equations (DDEs) and their extension to include stochastic effects. DDEs do not explicitly incorporate intermediate states associated with a delayed process but instead use an estimated average delay time. In an effort to examine the validity of this approach, we study systems with significant delays by explicitly incorporating intermediate steps. We show by that such explicit models often yield significantly different equilibrium distributions and transition times as compared to DDEs with deterministic delay values. Additionally, different explicit models with qualitatively different dynamics can give rise to the same DDEs revealing important ambiguities. We also show that DDE-based predictions of oscillatory behavior may fail for the corresponding explicit model.

A short note on delay effects in cell proliferation

1995

We show that a growth model for cell proliferation that incorporates a time-lag in the cell division phase is more consistent with certain reported data than the classical exponential growth model. Although both models provide estimates of some of the growth characteristics, such as the population doubling-time, the time-lag growth model additionally provides estimates of: (i) the cell-doubling time, (ii) the fraction of the cells that are dividing, (iii) the rate of commitment to cell division and, (iv) the initial distribution of cells in the cell cycle.

Delay effect in models of population growth

Journal of Mathematical Analysis and Applications, 2005

First, we systematize earlier results on the global stability of the modelẋ + µx = f (x(· − τ )) of population growth. Second, we investigate the effect of delay on the asymptotic behavior when the nonlinearity f is a unimodal function. Our results can be applied to several population mod-

Theory For Transitions Between Exponential And Stationary Phases: Universal Laws For Lag Time

2017

The quantitative characterization of bacterial growth has attracted substantial research attention since Monods pioneering study. Theoretical and experimental work have uncovered several laws for describing the exponential growth phase, in which the number of cells grows exponentially. However, microorganism growth also exhibits lag, stationary, and death phases under starvation conditions, in which cell growth is highly suppressed, for which quantitative laws or theories are markedly underdeveloped. In fact, the models commonly adopted for the exponential phase that consist of autocatalytic chemical components, including ribosomes, can only show exponential growth or decay in a population, and thus phases that halt growth are not realized. Here, we propose a simple, coarse-grained cell model that includes an extra class of macromolecular components in addition to the autocatalytic active components that facilitate cellular growth. These extra components form a complex with the acti...