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A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

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Published on: July 25, 2017

mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.

Kris Sankaran¹, Pratheepa Jeganathan²

¹Department of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, United States of America.

Plos Computational Biology

|June 14, 2024

Summary

This summary is machine-generated.

We developed novel transfer function models for microbial community dynamics, improving temporal memory and accuracy in predicting intervention effects. This approach helps identify key microbial taxa impacted by environmental changes.

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Published on: October 15, 2019

Area of Science:

Microbiology
Computational Biology
Systems Biology

Background:

Microbiome time series studies are crucial for understanding microbial ecosystem structure.
Current models for microbial community dynamics have limitations in temporal memory and expressivity, often relying on Markov or linearity assumptions.
These limitations hinder accurate prediction of microbial responses to interventions.

Purpose of the Study:

To introduce a new class of models for microbial community dynamics based on transfer functions.
To capture delayed effects of environmental changes on microbial communities.
To enable simulation of trajectories under hypothetical interventions and identification of significantly perturbed taxa.

Main Methods:

Developed a novel class of models utilizing transfer functions to learn impulse responses.
Modeled the potentially delayed effects of environmental changes on microbial community structure.
Implemented False Discovery Rate (FDR) guarantees for selecting significantly perturbed taxa.
Utilized simulations to compare the new approach against existing baselines.

Main Results:

The transfer function models demonstrated reduced forecasting errors compared to strong baselines.
The approach accurately pinpointed taxa of interest, indicating effective identification of perturbed microbes.
Case studies showcased the interpretability of the differential response trajectories derived from the models.

Conclusions:

Transfer function models offer enhanced temporal memory and expressivity for microbial community dynamics.
This novel approach improves the accuracy of predicting intervention effects and identifying key microbial taxa.
The developed R package, mbtransfer, facilitates the application and replication of these methods.