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.
Highlights
The fundamental role of the so-called growth curve to understand the underlying physiology of microbial cultures received the attention by many authors in the last decades
The so-called growth curve is of help to understand the underlying physiology of microbial cultures
The present paper reports the formal details of the model and its extension to cases of environment changes occurred because of an exterior perturbation
Summary
The fundamental role of the so-called growth curve to understand the underlying physiology of microbial cultures received the attention by many authors in the last decades. The data to use (plate counts and/or Optical Density records) demand preliminary treatments, like dilution factors, conversion from OD to population density, and, above all, transfer of the observed values to logarithmic units, that widen the statistical uncertainty of the fitting parameters, which is related to the number of the data and their position along the growth progress [14]. In order to overcome this incongruence, a reduced number of fitting parameters is worth considering. With such an aim, previous works [15] [16] [17] [18] [19] presented a semi-empirical model that described the growth and the decay of batch microbial cultures. The reported data come from quoted papers where the reader can find the relevant experimental details
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
