  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.
