Predictive modeling of microbial single cells: A review
| Autor: | Shiguo Chen, Tian Ding, Donghong Liu, Xingqian Ye, Xiao-Ting Xuan, Qing-Li Dong, Xinyu Liao |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Mathematical model Potential risk business.industry Stochastic modelling Growth data 030106 microbiology General Medicine Biology Listeria monocytogenes Models Biological Industrial and Manufacturing Engineering Biotechnology 03 medical and health sciences Food chain 030104 developmental biology Food products Food Microbiology Biochemical engineering Predictive microbiology Single-Cell Analysis business Food Science |
| Zdroj: | Critical Reviews in Food Science and Nutrition. 58:711-725 |
| ISSN: | 1549-7852 1040-8398 |
| DOI: | 10.1080/10408398.2016.1217193 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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