Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.
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Cover Image
Cover Image
The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. These codes cannot be readily discovered and understood without the use of computational modelling and algorithms. In this issue (see pages 1519–1528), Bahiri-Elitzur and Tuller summarize and discuss the different approaches that have been employed in the field in recent years. This cover artwork has been created by Hagar Messer and was provided by Tamir Tuller.
Genome scale metabolic models and analysis for evaluating probiotic potentials
Yoon-Mi Choi, Yi Qing Lee, Hyun-Seob Song, Dong-Yup Lee; Genome scale metabolic models and analysis for evaluating probiotic potentials. Biochem Soc Trans 28 August 2020; 48 (4): 1309–1321. doi: https://doi.org/10.1042/BST20190668
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