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Gene Regulation in Microbial Communities: Quorum Sensing01:28

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Quorum sensing is a mechanism of bacterial communication that enables coordinated gene expression in response to changes in population density. This facilitates collective behaviors that enhance survival, resource acquisition, and ecological adaptation. This process relies on small signaling molecules called autoinducers that accumulate as bacterial populations grow. When a critical threshold concentration of autoinducers is reached, bacterial cells collectively modify gene expression,...
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Bacterial signaling can occur within bacteria (intracellular) or between bacteria (intercellular). At times, a group of bacteria behaves like a community. To achieve this, they engage in quorum sensing, the perception of higher cell density that causes changes in gene expression. Quorum sensing involves both extracellular and intracellular signaling. The signaling cascade starts with a molecule called an autoinducer (AI). Individual bacteria produce AIs that move out of the bacterial cell...
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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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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Video

Updated: Jan 1, 2026

Anti-virulent Disruption of Pathogenic Biofilms using Engineered Quorum-quenching Lactonases
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Bayesian inversion for a biofilm model including quorum sensing.

Leila Taghizadeh1, Ahmad Karimi1, Elisabeth Presterl2

  • 1Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria.

Computers in Biology and Medicine
|December 31, 2019
PubMed
Summary

We developed a mathematical model for biofilm growth and cooperation, incorporating environmental factors and quorum sensing (QS). Our model accurately predicts biofilm behavior, validated by experimental data, aiding in diverse applications from medical implants to industrial processes.

Keywords:
Bayesian inversionExistence and uniquenessGrowth and degradation of biofilmsInverse problemPartial differential equationQuorum sensingUncertainty quantification

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Area of Science:

  • Mathematical Biology
  • Microbiology
  • Biophysics

Background:

  • Biofilms are complex microbial communities with significant implications across various industries and health sectors.
  • Understanding biofilm dynamics, including growth, degradation, and cooperative behaviors, is crucial for effective management and control.
  • Quorum sensing (QS) plays a vital role in bacterial communication and coordinated responses within biofilms, particularly in resisting external challenges like antibiotics.

Purpose of the Study:

  • To develop and validate a novel mathematical model for simulating biofilm evolution, incorporating environmental factors and quorum sensing (QS).
  • To analyze the cooperative behavior of bacteria within biofilms under stress conditions.
  • To determine unknown model parameters using inverse problem techniques and validate the model against experimental data.

Main Methods:

  • Formulation of a partial differential equation (PDE) system to model biofilm growth, degradation, and QS-mediated cooperation.
  • Mathematical analysis to establish the existence and uniqueness of solutions.
  • Numerical simulations to explore biofilm dynamics.
  • Application of Bayesian inversion techniques and the delayed-rejection adaptive-Metropolis (DRAM) algorithm for parameter estimation.
  • Model validation through comparison of simulation results with experimental measurements.

Main Results:

  • The proposed PDE model successfully describes the time evolution of biofilm growth and degradation influenced by environmental factors.
  • The model captures quorum sensing (QS)-driven bacterial cooperation for enhanced resistance against external factors.
  • Parameter estimation using Bayesian inversion and DRAM algorithm yielded accurate values for unknown model parameters.
  • Simulations based on estimated parameters showed excellent agreement with experimental measurement data, validating the model's predictive power.

Conclusions:

  • The developed mathematical model provides a robust framework for understanding and predicting biofilm behavior.
  • The integration of QS in the model highlights its importance in bacterial cooperation and resistance mechanisms.
  • The successful parameterization and validation demonstrate the model's utility for applications in diverse fields such as medicine, industry, and environmental science.