Literature DB >> 14984774

Estimating the bacterial lag time: which model, which precision?

Florent Baty1, Marie-Laure Delignette-Muller.   

Abstract

The objective of this work was to explore the large number of bacterial growth models recently proposed in the field of predictive microbiology, concerning their capacity to give reliable estimates of the lag phase duration (lambda). We compared these models on the basis of their underlying biological explanations of the lag phenomenon, their mathematical formulation and their statistical fitting properties. Results show that a variety of biological interpretations of the lag phase exists, although different biological hypotheses sometimes converge to give identical mathematical equations. The fit of the different models provides relatively close lambda estimates, especially if we consider that the imprecision of the lambda estimates is generally larger than the differences between the models. In addition, the consistency of the lambda estimates closely depends on the quality of the dataset on which models were fitted.

Mesh:

Year:  2004        PMID: 14984774     DOI: 10.1016/j.ijfoodmicro.2003.07.002

Source DB:  PubMed          Journal:  Int J Food Microbiol        ISSN: 0168-1605            Impact factor:   5.277


  27 in total

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Authors:  K Zhou; S M George; A Métris; P L Li; J Baranyi
Journal:  Appl Environ Microbiol       Date:  2010-12-30       Impact factor: 4.792

2.  Use of stochastic models to assess the effect of environmental factors on microbial growth.

Authors:  José Miguel Ponciano; Frederik P J Vandecasteele; Thomas F Hess; Larry J Forney; Ronald L Crawford; Paul Joyce
Journal:  Appl Environ Microbiol       Date:  2005-05       Impact factor: 4.792

3.  Genome-wide transcriptional responses to carbon starvation in nongrowing Lactococcus lactis.

Authors:  Onur Ercan; Michiel Wels; Eddy J Smid; Michiel Kleerebezem
Journal:  Appl Environ Microbiol       Date:  2015-01-30       Impact factor: 4.792

4.  Rapid biosensor for detection of antibiotic-selective growth of Escherichia coli.

Authors:  Karin Y Gfeller; Natalia Nugaeva; Martin Hegner
Journal:  Appl Environ Microbiol       Date:  2005-05       Impact factor: 4.792

Review 5.  Lag Phase Is a Dynamic, Organized, Adaptive, and Evolvable Period That Prepares Bacteria for Cell Division.

Authors:  Robert L Bertrand
Journal:  J Bacteriol       Date:  2019-03-13       Impact factor: 3.490

6.  A random effect multiplicative heteroscedastic model for bacterial growth.

Authors:  Ricardo Cao; Mario Francisco-Fernández; Emiliano J Quinto
Journal:  BMC Bioinformatics       Date:  2010-02-08       Impact factor: 3.169

7.  Glycine Betaine Effect on Dormancy in Deinococcus sp. UDEC-P1 and Psychrobacter sp. UDEC-A5 Exposed to Hyperosmotic Stress.

Authors:  Karina Gonzalez; Boris Parra; Carlos T Smith; Miguel Martinez
Journal:  Curr Microbiol       Date:  2019-09-07       Impact factor: 2.188

8.  A method for estimating the time of initiating correct categorization in mouse-tracking.

Authors:  David S March; Lowell Gaertner
Journal:  Behav Res Methods       Date:  2021-04-12

9.  Mathematical Models for the Biofilm Formation of Geobacillus and Anoxybacillus on Stainless Steel Surface in Whole Milk.

Authors:  Basar Karaca; Sencer Buzrul; Arzu Coleri Cihan
Journal:  Food Sci Anim Resour       Date:  2021-03-01

10.  Growth comparison of several Escherichia coli strains exposed to various concentrations of lactoferrin using linear spline regression.

Authors:  Camilla Sekse; Jon Bohlin; Eystein Skjerve; Gerd E Vegarud
Journal:  Microb Inform Exp       Date:  2012-04-16
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