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Microbiome modeling: a beginner's guide.

Emanuel Lange^1,2, Lena Kranert³, Jacob Krüger⁴

¹Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.

Frontiers in Microbiology

|July 4, 2024

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This summary is machine-generated.

This review bridges the knowledge gap between microbiologists and computational modelers. It provides an interdisciplinary overview of microbiome modeling, from metaomics data to predictive models for microbiome control.

Area of Science:

Microbiome research
Systems biology
Computational modeling

Background:

Microbiomes are complex microbial ecosystems crucial for health, environment, and biotechnology.
Current understanding is limited by complexity, necessitating advanced analytical approaches.
Systems biology offers a holistic framework for studying biological systems.

Purpose of the Study:

To bridge the interdisciplinary knowledge gap between microbiologists and computational modelers.
To provide an accessible overview of microbiome modeling for researchers and bioinformaticians.
To facilitate the development and application of computational models in microbiome research.

Main Methods:

Utilizing systems biology approaches for holistic microbiome analysis.

Keywords:

bioinformatics computational biology constraint-based modeling genome-scale modeling human microbiome microbial ecology omics data integration systems microbiology

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Employing metaomics methods to generate molecular feature data.

Integrating experimental data into computational microbiome models.

Main Results:

Metaomics data can be compiled into computational models.
Models enable prediction, optimization, and control of microbiome behavior.
The review offers entry-level information, examples, and references for microbiome modeling.

Conclusions:

Computational modeling is essential for advancing microbiome research and applications.
Interdisciplinary knowledge sharing is key to overcoming current limitations.
This review serves as a foundational resource for microbiome modeling.