In practice, food products tend to be contaminated with food-borne pathogens at a low inoculum level. However, the huge potential risk cannot be ignored because microbes may initiate high-speed growth suitable conditions during the food chain, such as transportation or storage. Thus, it is important to perform predictive modeling of microbial single cells. Several key aspects of microbial single-cell modeling are covered in this review. First, based on previous studies, the techniques of microbial single-cell data acquisition and growth data collection are presented in detail. In addition, the sources of microbial single-cell variability are also summarized. Due to model microbial growth, traditional deterministic mathematical models have been developed. However, most models fail to make accurate predictions at low cell numbers or at the single-cell level due to high cell-to-cell heterogeneity. Stochastic models have been a subject of great interest; and these models take into consideration the variability in microbial single-cell behavior.

中文翻译：

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微生物单细胞的预测建模：综述

在实践中，食品在低接种量的情况下容易被食源性病原体污染。但是，巨大的潜在风险不容忽视，因为微生物可能在食物链中引发高速增长的适宜条件，例如运输或存储。因此，对微生物单细胞进行预测建模很重要。这篇综述涵盖了微生物单细胞建模的几个关键方面。首先，在先前研究的基础上，详细介绍了微生物单细胞数据采集和生长数据收集技术。另外，还总结了微生物单细胞变异性的来源。由于模型微生物的生长，已经开发了传统的确定性数学模型。然而，由于细胞间异质性高，大多数模型无法在低细胞数或单细胞水平上做出准确的预测。随机模型引起了人们的极大兴趣。并且这些模型考虑了微生物单细胞行为的变异性。