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Poschet, F., Bernaerts, K., Geeraerd, A.H., Scheerlink, N., Nicolaı, J.F. and Van Impe, J.F. (2004) Sensitivity Analysis of Microbial Growth Parameter Distributions with Respect of Data Quality and Quantity by Using Monte Carlo Analysis. Mathematics and Computers in Simulation, 65, 231-243.
https://doi.org/10.1016/j.matcom.2003.12.002

has been cited by the following article:

• TITLE: Batch Microbial Cultures: A Model That Can Account for Environment Changes

  AUTHORS: Alberto Schiraldi

  KEYWORDS: Microbial Cultures, Empirical Model, Environment Changes, Predictive Microbiology

  JOURNAL NAME: Advances in Microbiology, Vol.11 No.11, November 23, 2021

  ABSTRACT: The so-called growth curve is of help to understand the underlying physiology of microbial cultures. A number of reported models describe the observed growth trends and the effects produced by the changes of the culture environment. However, the collected data (plate counts and/or Optical Density records) very often do not reliably comply with the number of fitting parameters of such models. An alternative semi empirical model describes the observed experimental trends of growth and decay of batch microbial cultures. Major advantages of the model include: reduced number and direct physical meaning of the best-fit parameters, easy comparison between different microbial cultures and/or different environment conditions for a given microbial strain. The experimental data (either plate counts or OD records) allow the estimation of the fitting parameters: that is why the model is substantially empirical and applies to any batch microbial culture. The present paper reports the formal details of the model and its extension to cases of environment changes occurred because of an exterior perturbation. The model seems adequate for predictive microbiology investigations, as well as for studies on the effects of bactericidal drugs.