Neo-logistic model for the growth of bacteria

TL;DR

The paper introduces a neo-logistic model that accurately describes bacterial growth by incorporating enzyme synthesis, providing clearer parameter interpretation and improved prediction of stationary phase bacterial counts.

Contribution

A novel neo-logistic model that integrates enzyme synthesis, enhancing bacterial growth modeling and parameter interpretability compared to existing models.

Findings

Better approximation of bacterial growth curves.

More accurate prediction of stationary phase bacterial counts.

Parameters have clear physical meaning.

Abstract

We propose a neo-logistic model that can describe bacterial growth data precisely. This model is not derived by modifying the logistic model formally, but by incorporating the synthesis of inducible enzymes into the logistic model indirectly. Therefore, the meaning of the parameters of the neo-logistic model becomes physically clear. The neo-logistic model can approximate bacterial growth better than models previously presented, and predict the order of the saturated number of bacteria in the stationary phase from initial data including, and just after the end of, the lag phase much more accurately.