Neo-logistic model for the growth of bacteria

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.