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Biofilms

Biofilms

Biofilms are complex communities of microorganisms encased in a self-produced extracellular polysaccharide matrix attached to surfaces. These microbial consortia can include single or multiple species, providing enhanced survival benefits by forming organized, multilayered structures.The formation of biofilms occurs through four key stages: attachment, colonization, development, and dispersal.During attachment, free-swimming planktonic cells adhere to a surface, often facilitated by...

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Related Experiment Video

Updated: Jul 4, 2026

Use of a High-throughput In Vitro Microfluidic System to Develop Oral Multi-species Biofilms

Use of a High-throughput In Vitro Microfluidic System to Develop Oral Multi-species Biofilms

Published on: December 1, 2014

A multispecies biofilm model.

O Wanner¹, W Gujer

¹Swiss Federal Institute for Water Resources and Water Pollution Control (EAWAG), CH-8600 Dübendorf, Switzerland.

Biotechnology and Bioengineering

|March 1, 1986

Summary

A new mathematical model analyzes microbial biofilm dynamics, predicting thickness changes and species distribution. This model accounts for detachment and substrate variations, aiding in understanding biofilm performance.

Area of Science:

Microbiology
Biochemical Engineering
Mathematical Modeling

Background:

Biofilms are complex microbial communities crucial in various environments.
Understanding biofilm dynamics is essential for applications ranging from medicine to industrial processes.
Existing models often simplify the intricate interactions within biofilms.

Purpose of the Study:

To develop a comprehensive analytical mathematical model for microbial interactions in biofilms.
To predict biofilm thickness, microbial distribution, and substrate dynamics.
To incorporate key factors like biomass detachment and mass transfer limitations.

Main Methods:

A continuum approach based on conservation principles was employed.
The model analytically describes spatial distribution and temporal changes.

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Computer simulations were used to demonstrate model capabilities with a competition example.

Main Results:

The model successfully predicts biofilm thickness variations.
It illustrates the spatial distribution of microbial species and substrates.
Numerical reproduction of phenomena like detachment and substrate concentration changes was achieved.

Conclusions:

The developed model provides a robust framework for studying biofilm behavior.
It highlights the impact of various factors on overall biofilm performance.
The model serves as a valuable tool for predicting and managing biofilms.