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Related Experiment Video

Updated: Apr 21, 2026

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

Active Brownian agents with concentration-dependent chemotactic sensitivity.

Marcel Meyer¹, Lutz Schimansky-Geier¹, Pawel Romanczuk²

¹Department of Physics, Humboldt Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|October 30, 2014

Summary

This summary is machine-generated.

This study models how self-moving agents form patterns based on chemical signals. Different sensitivities to these signals lead to distinct structures like clusters and bubbles, impacting agent behavior.

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Last Updated: Apr 21, 2026

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

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Imaging G Protein-coupled Receptor-mediated Chemotaxis and its Signaling Events in Neutrophil-like HL60 Cells

Imaging G Protein-coupled Receptor-mediated Chemotaxis and its Signaling Events in Neutrophil-like HL60 Cells

Published on: September 14, 2016

Area of Science:

Biophysics
Chemical Physics
Mathematical Biology

Background:

Autochemotaxis involves agents moving in response to self-produced chemical signals.
Understanding pattern formation in biological systems is crucial for explaining collective behaviors.

Purpose of the Study:

To investigate how concentration-dependent chemotactic sensitivity influences large-scale pattern formation.
To compare different models of chemotactic sensitivity (receptor law, constant, log law) for their impact on pattern emergence.

Main Methods:

Derivation of a coarse-grained macroscopic description from an individual-based model.
Analysis of stability for homogeneous solutions and conditions for inhomogeneous solutions.
Large-scale GPU simulations of the individual-based model.

Main Results:

Qualitative differences observed in pattern formation (labyrinthine structures, clusters, bubbles) based on chemotactic sensitivity variants.
Spinodal decomposition and nucleation identified as mechanisms for pattern emergence.
Two limiting cases analyzed, reducing the model to single-species dynamics.

Conclusions:

Chemotactic sensitivity significantly impacts pattern formation and stability in autochemotactic systems.
The receptor law exhibits distinct behavior compared to constant and log law sensitivities.
The study provides insights into collective behavior and domain growth in agent-based systems.