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Related Concept Videos

Gene Regulation in Microbial Communities: Quorum Sensing01:28

Gene Regulation in Microbial Communities: Quorum Sensing

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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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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
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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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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
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Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...
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Cyclic Feedback Systems with Quorum Sensing Coupling.

Tomas Gedeon1, Mark Pernarowski2, Adam Wilander2

  • 1Department of Mathematical Sciences, Montana State University, Bozeman, MT, 59715, USA. gedeon@math.montana.edu.

Bulletin of Mathematical Biology
|June 23, 2016
PubMed
Summary
This summary is machine-generated.

Collective dynamics in biological systems depend on signaling integration. Negative feedback, negative signaling systems show stable equilibrium or oscillation, while positive signaling feedback leads to multistability.

Keywords:
MultistabilityNegative cyclic feedback systemsQuorum sensingRepressilator

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Area of Science:

  • Systems biology
  • Theoretical biology
  • Biophysics

Background:

  • Synchronization and desynchronization are key in circadian rhythms, metabolic oscillations, and cell behaviors.
  • Repressilator models coupled via quorum sensing with diffusive signals exhibit complex dynamics sensitive to signaling pathways.

Purpose of the Study:

  • To rigorously prove how signaling network integration into repressilator networks affects collective dynamics.
  • To provide a general result for negative cyclic feedback systems with signaling, including repressilators.

Main Methods:

  • Mathematical modeling and rigorous proof.
  • Analysis of negative feedback, negative signaling (Nf-Ns) systems.
  • Investigation of systems with positive signaling feedback.

Main Results:

  • Nf-Ns systems exhibit either a unique stable equilibrium or a stable oscillation.
  • Inclusion of positive signaling feedback disrupts the Nf-Ns property, leading to multistable dynamics.
  • Multistability arises from saddle-node bifurcations of a cubic curve, similar to generic bistable models.

Conclusions:

  • The integration of signaling networks critically determines the collective dynamics of biological systems.
  • Negative signaling feedback promotes stable states (equilibrium or oscillation).
  • Positive signaling feedback introduces complex multistability, highlighting the importance of feedback loop topology.