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Genome scale metabolic models and analysis for evaluating probiotic potentials.

Yoon-Mi Choi1, Yi Qing Lee1, Hyun-Seob Song2

  • 1School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Republic of Korea.

Biochemical Society Transactions
|July 30, 2020
PubMed
Summary

Selecting effective probiotic strains for gut health is challenging. In silico systems biology approaches using genome-scale metabolic models (GEMs) can predict probiotic capabilities, aiding personalized probiotic formulation.

Keywords:
in silico analysisgenome scale metabolic modelmulti-strain probioticsstrain specificitysystems biology

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Area of Science:

  • Microbiology
  • Systems Biology
  • Computational Biology

Background:

  • Probiotics, live beneficial microorganisms, promote gut health but selecting effective strains for multi-strain blends is challenging.
  • Current in vivo and in vitro methods have limitations in predicting probiotic efficacy across diverse human gut environments.

Purpose of the Study:

  • To summarize available genome-scale metabolic models (GEMs) for microbial strains with probiotic potential.
  • To propose a knowledge-based framework for evaluating probiotic capabilities using in silico methods.

Main Methods:

  • Review of existing GEMs for probiotic microorganisms.
  • Development of a framework assessing six key probiotic criteria: metabolic characteristics, stability, safety, colonization, postbiotics, and microbiome interaction.
  • Utilizing in silico approaches for evaluation.

Main Results:

  • Currently available GEMs for probiotic strains are summarized.
  • A framework is proposed to evaluate metabolic capabilities based on six essential probiotic criteria.
  • In silico methods can assess these criteria to predict strain suitability.

Conclusions:

  • In silico systems biology approaches, particularly using GEMs, offer a powerful tool to overcome limitations of traditional methods for probiotic strain evaluation.
  • The proposed framework enables objective assessment of probiotic potential, facilitating the identification of suitable strains.
  • This approach paves the way for designing personalized multi-strain probiotics with consistent efficacy.