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Related Experiment Video

Updated: May 26, 2026

A High Throughput Screen for Biomining Cellulase Activity from Metagenomic Libraries

A High Throughput Screen for Biomining Cellulase Activity from Metagenomic Libraries

Published on: February 2, 2011

**Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85**.

Ibrahim Fakih^1,2, Jeanne Got³, Carlos Eduardo Robles-Rodriguez⁴

¹Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé , 63000 Clermont-Ferrand, France.

|June 8, 2023

Summary

Dynamic models of Fibrobacter succinogenes metabolism were developed using genome data. These models predict fermentation processes, aiding in rumen microbiome research and industrial applications.

Keywords:

dynamic model elementary flux mode analysis fiber degradation genome-scale metabolic model network reconstruction rumen fermentation

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Published on: January 13, 2017

Area of Science:

Microbial Metabolism
Systems Biology
Rumen Microbiology

Background:

Fibrobacter succinogenes is a key rumen bacterium for plant fiber degradation.
It metabolizes cellulose into glycogen and fermentation products like succinate.
Understanding its metabolism is crucial for ruminant feed efficiency and industrial biotechnology.

Purpose of the Study:

To develop dynamic metabolic models for Fibrobacter succinogenes S85.
To simulate its metabolism on different carbon sources (glucose, cellobiose, cellulose).
To provide a resource for studying rumen fermentation and microbial manipulation.

Main Methods:

Metabolic network reconstruction using genome annotation and computational tools.
Model reduction with the NetRed algorithm and elementary flux mode analysis.
Yield analysis to identify key metabolic reactions for each substrate.

Main Results:

A comprehensive metabolic network for F. succinogenes S85 was reconstructed (1,565 reactions, 1,317 genes).
Dynamic models accurately simulated carbohydrate metabolism (19% RMSE variation).
Models enable investigation of metabolite production dynamics.

Conclusions:

The developed models are valuable for understanding F. succinogenes S85 metabolic capabilities.
This approach facilitates the integration of omics data into predictive rumen metabolism models.
The methodology can be extended to other rumen microbes for microbiome modeling and optimization.