Literature DB >> 9217107

A heterogeneous population model for the analysis of bacterial growth kinetics.

R C McKellar1.   

Abstract

A two-compartment, heterogeneous population model (HPM) was derived using the simulation software SB ModelMaker to describe the growth of Listeria monocytogenes in bacteriological media at 5-35 degrees C. The model assumed that, at time t = 0, the inoculum was distributed between two distinct compartments, Non-Growing and Growing, and that growth could be described by four parameters: initial total cell population (N0), final maximum cell population (Nmax), maximum specific growth rate (mu(max)), and initial cell population in the Growing compartment (G0). The model was fitted to the data by optimizing the four parameters, and lag phase duration (lambda) was calculated. The resulting values of mu(max) and lambda were similar to those determined using the modified Gompertz equation. A new parameter, w0, was defined which relates to the proportion of the initial cell population capable of growth, and is a measure of the initial physiological state of the cells. A modified model in which mu(max) was replaced with a temperature function, and w0 replaced G0, was used to predict the effect of temperature on the growth of L. monocytogenes. The results of this study raise questions concerning the current definition of the lag phase.

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Year:  1997        PMID: 9217107     DOI: 10.1016/s0168-1605(97)01266-x

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


  8 in total

1.  Lag phase of Salmonella enterica under osmotic stress conditions.

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.  Influence of stress on individual lag time distributions of Listeria monocytogenes.

Authors:  L Guillier; P Pardon; J-C Augustin
Journal:  Appl Environ Microbiol       Date:  2005-06       Impact factor: 4.792

Review 3.  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

4.  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

5.  Mathematical Modeling of Biofilm Structures Using COMSTAT Data.

Authors:  Davide Verotta; Janus Haagensen; Alfred M Spormann; Katherine Yang
Journal:  Comput Math Methods Med       Date:  2017-12-20       Impact factor: 2.238

6.  A model for predicting Xanthomonas arboricola pv. pruni growth as a function of temperature.

Authors:  Gerard Morales; Isidre Llorente; Emilio Montesinos; Concepció Moragrega
Journal:  PLoS One       Date:  2017-05-11       Impact factor: 3.240

7.  Modeling of starter cultures growth for improved Thai sausage fermentation and cost estimating for sausage preparation and transportation.

Authors:  Wiramsri Sriphochanart; Wanwisa Skolpap
Journal:  Food Sci Nutr       Date:  2018-06-28       Impact factor: 2.863

8.  Environmental and Physiological Factors Affecting High-Throughput Measurements of Bacterial Growth.

Authors:  Esha Atolia; Spencer Cesar; Heidi A Arjes; Manohary Rajendram; Handuo Shi; Benjamin D Knapp; Somya Khare; Andrés Aranda-Díaz; Richard E Lenski; Kerwyn Casey Huang
Journal:  mBio       Date:  2020-10-20       Impact factor: 7.867

  8 in total

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