Literature DB >> 18329047

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

Clara Prats1, Antoni Giró, Jordi Ferrer, Daniel López, Josep Vives-Rego.   

Abstract

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 lambda. 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. In this paper, the definition of lag phase is thoroughly examined. Evolution of the total biomass and the total number of bacteria during lag phase is tackled separately. The lag phase lasts until the culture reaches a maximum growth rate both in biomass and cell density. Once in the exponential phase, both rates are constant over time and equal to each other. Both evolutions are split into an initial phase and a transition phase, according to their growth rates. A population-level mathematical model is presented to describe the transitional phase in cell density. INDividual DIScrete SIMulation (INDISIM) is used to check the outcomes of this analysis. Simulations allow the separate study of the evolution of cell density and total biomass in a batch culture, they provide a depiction of different observed cases in lag evolution at the individual-cell level, and are used to test the population-level model. The results show that the geometrical lag parameter lambda is not appropriate as a universal definition for the lag phase. Moreover, the lag phase cannot be characterized by a single parameter. For the studied cases, the lag phases of both the total biomass and the population are required to fully characterize the evolution of bacterial cultures. The results presented prove once more that the lag phase is a complex process that requires a more complete definition. This will be possible only after the phenomena governing the population dynamics at an individual level of description, and occurring during the lag and exponential growth phases, are well understood.

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Year:  2008        PMID: 18329047     DOI: 10.1016/j.jtbi.2008.01.019

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  7 in total

1.  Analysis of the effect of inoculum characteristics on the first stages of a growing yeast population in beer fermentations by means of an individual-based model.

Authors:  M Ginovart; C Prats; X Portell; M Silbert
Journal:  J Ind Microbiol Biotechnol       Date:  2010-09-03       Impact factor: 3.346

2.  RecT Affects Prophage Lifestyle and Host Core Cellular Processes in Pseudomonas aeruginosa.

Authors:  Xiang Long; Hanhui Zhang; Xiaolong Wang; Daqing Mao; Weihui Wu; Yi Luo
Journal:  Appl Environ Microbiol       Date:  2022-09-08       Impact factor: 5.005

3.  Skew-laplace and cell-size distribution in microbial axenic cultures: statistical assessment and biological interpretation.

Authors:  Olga Julià; Jaume Vidal-Mas; Nicolai S Panikov; Josep Vives-Rego
Journal:  Int J Microbiol       Date:  2010-06-01

4.  Mathematical modeling of tuberculosis bacillary counts and cellular populations in the organs of infected mice.

Authors:  Antonio Bru; Pere-Joan Cardona
Journal:  PLoS One       Date:  2010-09-23       Impact factor: 3.240

5.  Low dose aerosol fitness at the innate phase of murine infection better predicts virulence amongst clinical strains of Mycobacterium tuberculosis.

Authors:  Neus Caceres; Isaac Llopis; Elena Marzo; Clara Prats; Cristina Vilaplana; Dario García de Viedma; Sofía Samper; Daniel Lopez; Pere-Joan Cardona
Journal:  PLoS One       Date:  2012-01-03       Impact factor: 3.240

6.  Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling.

Authors:  Marta Ginovart; Rosa Carbó; Mónica Blanco; Xavier Portell
Journal:  Front Microbiol       Date:  2018-01-04       Impact factor: 5.640

Review 7.  Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods.

Authors:  Panagiotis N Skandamis; Sophie Jeanson
Journal:  Front Microbiol       Date:  2015-10-29       Impact factor: 5.640

  7 in total

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