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Updated: Feb 26, 2026

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Published on: February 16, 2020

Macroscopic modelling and analysis based on microscopic models for swarm systems.

Quan Quan^1,2, Xinchen Yu³, Yue Li^4,5

¹Tianmushan Laboratory, Beihang University, Hangzhou, 311115, China. qq_buaa@buaa.edu.cn.

Scientific Reports

|February 24, 2026

Summary

This summary is machine-generated.

This study introduces a new bidirectional closed-loop framework for modeling swarm systems. This approach integrates microscopic and macroscopic models, enhancing system analysis and optimization for complex swarm behaviors.

Keywords:

Bidirectional closed-loop modelling Macroscopic model Swarm intelligence

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Last Updated: Feb 26, 2026

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Bioparticle Microarrays for Chemotactic and Molecular Analysis of Human Neutrophil Swarming in vitro

Published on: February 16, 2020

Preparation, Imaging, and Quantification of Bacterial Surface Motility Assays

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Published on: April 7, 2015

Time-lapse Imaging of Bacterial Swarms and the Collective Stress Response

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Published on: May 23, 2020

Area of Science:

Complex Systems
Computational Modeling
Systems Biology

Background:

Swarm systems (biological and artificial) offer superior efficiency and robustness.
Existing modeling and analysis methodologies for swarm systems lack universality and comprehensiveness.
Bridging micro and macro levels in swarm modeling remains a challenge.

Purpose of the Study:

To develop a universal bidirectional closed-loop modeling framework for swarm systems.
To integrate microscopic and macroscopic modeling approaches.
To enhance the analysis and optimization of swarm behaviors.

Main Methods:

Utilized probabilistic finite state machines for microscopic model development.
Constructed macroscopic models using rate equations.
Implemented a feedback loop where macroscopic model evolution refines microscopic model configurations.

Main Results:

Validated the macroscopic model's effectiveness through a simulation of infectious disease transmission.
Demonstrated the framework's ability to derive macroscopic insights from microscopic details.
Showcased the macroscopic model's predictive power for long-term swarm behavior and optimization potential.

Conclusions:

The proposed bidirectional framework effectively bridges micro and macro levels in swarm system analysis.
Macroscopic models derived from this framework facilitate system optimization and improve decision-making efficiency.
This research provides a novel theoretical foundation for practical swarm behavior analysis and optimization.