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Active mixtures in a narrow channel: motility diversity changes cluster sizes
Pablo de Castro1, Saulo Diles2, Rodrigo Soto1
1Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Avenida Blanco Encalada 2008, Santiago, Chile. pdecastro@ing.uchile.cl.
Soft Matter
|January 21, 2021
Summary
Motility diversity in bacteria significantly impacts cluster size, with higher diversity leading to larger clusters. Accounting for this polydispersity is crucial for accurate bacterial cluster size distribution (CSD) estimations.
Area of Science:
- Active Matter Physics
- Statistical Mechanics
- Microbiology
Background:
- Persistent motion of bacteria leads to the formation of clusters with a stationary cluster size distribution (CSD).
- Understanding factors influencing CSD, such as motility diversity and confinement, is key to modeling bacterial behavior.
Purpose of the Study:
- To develop a minimal model assessing the importance of motility diversity and confinement on bacterial clusters in a narrow channel.
- To quantify the impact of polydispersity in tumbling rates on cluster size and CSD.
Main Methods:
- A 1D lattice model simulating run-and-tumble particles with a distribution of tumbling rates (α).
- Analysis of particle interactions, including crossing at a finite rate, to mimic quasi-1D confinement.
- Development of an effective theory to derive expressions for CSD and other key parameters.
Main Results:
- Motility diversity increases average cluster size (Lc) when particle crossing is absent, as slower particles trap faster ones.
- Finite crossing rates reduce Lc and the importance of diversity, though they can enhance phase separation.
- Model predictions, using Escherichia coli parameters, show a ~60% error in Lc estimation if polydispersity is ignored.
Conclusions:
- Motility diversity is a critical factor influencing bacterial cluster formation and CSD.
- Accurate modeling of bacterial systems requires accounting for polydispersity in motility parameters.
- The developed effective theory provides accurate predictions for CSD and other relevant metrics.

