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Published on: August 30, 2016
A simulation of microbial competition in the human colonic ecosystem
M E Coleman1, D W Dreesen, R G Wiegert
1Food Safety and Inspection Service, U.S. Department of Agriculture, Washington, DC 20250-3700, USA. usdafsis/g=p/s=coleman@mhs.attmail.com
Applied and Environmental Microbiology
|October 1, 1996
Summary
This study models microbial competition in the gut using a C-language simulation. The model predicts how different bacteria, like E. coli and Salmonella, utilize resources, aiding research into gut microbial ecology and food-borne diseases.
Area of Science:
- Microbial Ecology
- Computational Biology
- Gastrointestinal Microbiology
Background:
- Continuous-flow anaerobic cultures are common for studying gut microbial interactions.
- Understanding microbial competition is crucial for gastrointestinal health and disease.
Purpose of the Study:
- To develop and utilize a deterministic 11-compartment computer model simulating microbial competition in the human gastrointestinal tract.
- To analyze resource partitioning among six key microbial species under varying conditions.
Main Methods:
- A C programming language model was developed using published in vitro data and assumptions for missing parameters.
- The model simulated interactions between Escherichia coli, Enterobacter aerogenes, Bacteroides ovatus, Fusobacterium varium, Enterococcus faecalis, and Salmonella typhimurium.
- Resource (glucose, lactose, starch, etc.) and space limitations influenced carbon flow and resource partitioning based on feeding preference functions.
Main Results:
- The simulation calculated resource partitioning based on resource availability and microbial feeding preferences.
- Resources did not accumulate within the model iterations, reflecting competitive dynamics.
- The model provided insights into microbial competition dynamics within a simulated colonic ecosystem.
Conclusions:
- The computer simulation offers a tool for understanding microbial competition in the gut.
- Results, though based on limited measured parameters, can inform the design of software for manipulating colonic microbial ecology.
- This approach may aid in addressing food-borne diseases by understanding their ecological basis.
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