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Related Concept Videos

Microbial Nutrition01:28

Microbial Nutrition

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Organisms exhibit remarkable metabolic diversity, categorized based on how they acquire energy and carbon. These strategies enable survival in various ecological niches and are essential for maintaining energy flow and nutrient cycling within ecosystems.Energy and Carbon SourcesOrganisms are classified as phototrophs or chemotrophs based on energy acquisition. Phototrophs use light as their energy source, while chemotrophs rely on oxidizing chemical compounds. Further differentiation arises...
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Updated: Jun 5, 2025

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Designing host-associated microbiomes using the consumer/resource model.

Germán Plata1, Karthik Srinivasan2, Madan Krishnamurthy3

  • 1Computational Sciences, BiomEdit, LLC., Fishers, Indiana, USA.

Msystems
|December 9, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new generative model for host-associated microbiomes using the consumer/resource framework. This model accurately simulates microbial communities and links them to host phenotypes, aiding microbiome engineering.

Keywords:
consumer/resource modelgenerative modelinghost-associated microbiomes

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Area of Science:

  • Microbiome research
  • Computational biology
  • Host-microbiome interactions

Background:

  • Generative models are vital for biological system design, but lack models for host-associated microbiomes.
  • Existing models struggle with microbiome data variability and context-dependency.
  • Host-associated microbiomes significantly impact animal health and livestock productivity.

Purpose of the Study:

  • To develop a generative model for host-associated microbiomes.
  • To link microbial community composition with host phenotypes.
  • To enable rational microbiome engineering for desired host characteristics.

Main Methods:

  • Developed a generative model based on the mechanistic consumer/resource (C/R) framework.
  • Inferred latent variables representing resource availability to model species composition.
  • Used latent variables to model host phenotypic states.
  • Fitted the model to cross-sectional microbiome profile data from three animal hosts.

Main Results:

  • Generated realistic in silico microbial communities that reproduce key microbiome statistics.
  • Identified a latent space explaining microbial species abundance variations.
  • Successfully linked host phenotypes to microbiome compositions.
  • Demonstrated the model's ability to predict host-associated microbiomes and phenotypes.

Conclusions:

  • The C/R-based generative model effectively captures host-associated microbiome dynamics.
  • The model facilitates the analysis of phenotype/microbiome associations.
  • Enables the design of microbial communities for specific host phenotypes, advancing microbiome engineering.