Issue 16, 2024

Agent-based modeling of stress anisotropy driven nematic ordering in growing biofilms

Abstract

Living active collectives have evolved with remarkable self-patterning capabilities to adapt to the physical and biological constraints crucial for their growth and survival. However, the intricate process by which complex multicellular patterns emerge from a single founder cell remains elusive. In this study, we utilize an agent-based model, validated through single-cell microscopy imaging, to track the three-dimensional (3D) morphodynamics of cells within growing bacterial biofilms encased by agarose gels. The confined growth conditions give rise to a spatiotemporally heterogeneous stress landscape within the biofilm. In the core of the biofilm, where high hydrostatic and low shear stresses prevail, cell packing appears disordered. In contrast, near the gel–cell interface, a state of high shear stress and low hydrostatic stress emerges, driving nematic ordering, albeit with a time delay inherent to shear stress relaxation. Strikingly, we observe a robust spatiotemporal correlation between stress anisotropy and nematic ordering within these confined biofilms. This correlation suggests a mechanism whereby stress anisotropy plays a pivotal role in governing the spatial organization of cells. The reciprocity between stress anisotropy and cell ordering in confined biofilms opens new avenues for innovative 3D mechanically guided patterning techniques for living active collectives, which hold significant promise for a wide array of environmental and biomedical applications.

Graphical abstract: Agent-based modeling of stress anisotropy driven nematic ordering in growing biofilms

Supplementary files

Article information

Article type
Paper
Submitted
13 Nov 2023
Accepted
26 Feb 2024
First published
02 Apr 2024
This article is Open Access
Creative Commons BY-NC license

Soft Matter, 2024,20, 3401-3410

Agent-based modeling of stress anisotropy driven nematic ordering in growing biofilms

C. Li, J. Nijjer, L. Feng, Q. Zhang, J. Yan and S. Zhang, Soft Matter, 2024, 20, 3401 DOI: 10.1039/D3SM01535A

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