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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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Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Population Model of Quorum Sensing with Multiple Parallel Pathways.

Gaoyang Fan1, Paul C Bressloff2

  • 1Department of Mathematics, University of Utah, Salt Lake City, UT, 84112, USA.

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|September 10, 2017
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Summary

This study models bacterial quorum sensing (QS) in Vibrio harveyi, revealing how parallel signaling pathways integrate information at single-cell and population levels. Mathematical analysis simplifies complex population dynamics to understand group behaviors.

Keywords:
PhosphorylationQuorum sensingSignal integration

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Area of Science:

  • Microbiology
  • Systems Biology
  • Mathematical Biology

Background:

  • Quorum sensing (QS) regulates bacterial group behaviors like biofilm formation via cell density-dependent gene expression.
  • Vibrio harveyi employs multiple parallel QS pathways converging on a shared signaling cascade.
  • Understanding signal integration in QS is crucial for deciphering bacterial collective behaviors.

Purpose of the Study:

  • To develop a mathematical model for QS in Vibrio harveyi, examining signal integration at single-cell and population levels.
  • To investigate how different model parameters affect signal integration within individual bacterial cells.
  • To analyze signal integration in population-level models, including single populations responding to multiple cues and multi-population systems.

Main Methods:

  • Development of a mathematical model for quorum sensing in Vibrio harveyi.
  • Exploration of signal integration mechanisms at the single-cell level through parameter analysis.
  • Application of contraction analysis to reduce population-level models to effective single-cell models.
  • Simulation of one-population models with dual environmental cues (cell density, mass transfer) and two-population models with distinct densities.

Main Results:

  • The study elucidates mechanisms of signal integration in parallel QS pathways at the single-cell level.
  • Mathematical models demonstrate how environmental cues are processed and integrated by bacterial populations.
  • Contraction analysis successfully simplifies complex population dynamics into tractable single-cell equivalents.
  • Parameter variations significantly influence the signal integration process in the developed models.

Conclusions:

  • Parallel signaling pathways play a key role in achieving signal integration within bacterial quorum sensing systems.
  • Mathematical modeling provides a powerful framework for dissecting complex bacterial communication and group behaviors.
  • The developed models offer insights into how Vibrio harveyi coordinates gene expression in response to varying cell densities and environmental conditions.
  • Contraction analysis is an effective technique for reducing the complexity of multi-cell QS models.