Effect of lag time distribution on the lag phase of bacterial growth - a Monte Carlo analysis

Authors: Huang, Lihan

Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/23/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary: Lag phase is a bacterial growth phenomenon that affects the propagation of foodborne pathogens. This research employs a stochastic method to examine the contribution of individual bacterial cells to the formation of lag phase in a growth curve. Monte Carlo analysis shows that the lag phase formation is determined by the homogeneity of the bacterial culture. The results of this study may help choice and develop more suitable mathematical models for predicting the growth of foodborne pathogens in foods.

Technical Abstract: The objective of this study is to use Monte Carlo simulation to evaluate the effect of lag time distribution of individual bacterial cells incubated under isothermal conditions on the development of lag phase. The growth of bacterial cells of the same initial concentration and mean lag phase duration, but with different normally distributed individual lag times, is simulated. With each cell’s lag time counted during simulation, the results of Monte Carlo analysis show that the apparent lag phase duration of a growth curve is determined by the homogeneity of the bacterial culture, and the lag times of each individual cell are very narrowly distributed with the standard deviation less than 0.39% of the mean lag time. The apparent lag phase duration of a growth curve is equal to the mean lag time of the individual cells. For initial inoculums with greater than 1 cell, the lag phase expires when 50% of the lag cells leave the dormancy and begin to divide immediately. The results also show that the size of the initial inoculums does not affect of the apparent lag phase duration of a growth curve. The variations observed in the apparent lag phase are probably due to normal experimental errors. Any deterministic equation that can accurately depict the lag, exponential, and stationary phases of a growth curve can be used to model bacterial growth.