Self-organized fractal architectures driven by motility-dependent chemotactic feedback
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908334235320320 |
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| author | Khuntia, Subhashree Subhrasmita Chaudhuri, Debasish Chaudhuri, Abhishek |
| author_facet | Khuntia, Subhashree Subhrasmita Chaudhuri, Debasish Chaudhuri, Abhishek |
| contents | Complex spatial patterns in biological systems often arise through self-organization without a central coordination, guided by local interactions and chemical signaling. In this study, we explore how motility-dependent chemical deposition and concentration-sensitive feedback can give rise to fractal-like networks, using a minimal agent-based model. Agents deposit chemicals only while moving, and their future motion is biased by local chemical gradients. This interaction generates a rich variety of self-organized structures resembling those seen in processes like early vasculogenesis and epithelial cell dispersal. We identify a diverse phase diagram governed by the rates of chemical deposition and decay, revealing transitions from uniform distributions to sparse and dense networks, and ultimately to full phase separation. At low chemical decay rates, agents form stable, system-spanning networks; further reduction leads to re-entry into a uniform state. A continuum model capturing the co-evolution of agent density and chemical fields confirms these transitions and reveals how linear stability criteria determine the observed phases. At low chemical concentrations, diffusion dominates and promotes fractal growth, while higher concentrations favor nucleation and compact clustering. These findings unify a range of biological phenomena - such as chemotaxis, tissue remodeling, and self-generated gradient navigation - within a simple, physically grounded framework. Our results also offer insights into designing artificial systems with emergent collective behavior, including robotic swarms or synthetic active matter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-organized fractal architectures driven by motility-dependent chemotactic feedback Khuntia, Subhashree Subhrasmita Chaudhuri, Debasish Chaudhuri, Abhishek Biological Physics Soft Condensed Matter Statistical Mechanics Complex spatial patterns in biological systems often arise through self-organization without a central coordination, guided by local interactions and chemical signaling. In this study, we explore how motility-dependent chemical deposition and concentration-sensitive feedback can give rise to fractal-like networks, using a minimal agent-based model. Agents deposit chemicals only while moving, and their future motion is biased by local chemical gradients. This interaction generates a rich variety of self-organized structures resembling those seen in processes like early vasculogenesis and epithelial cell dispersal. We identify a diverse phase diagram governed by the rates of chemical deposition and decay, revealing transitions from uniform distributions to sparse and dense networks, and ultimately to full phase separation. At low chemical decay rates, agents form stable, system-spanning networks; further reduction leads to re-entry into a uniform state. A continuum model capturing the co-evolution of agent density and chemical fields confirms these transitions and reveals how linear stability criteria determine the observed phases. At low chemical concentrations, diffusion dominates and promotes fractal growth, while higher concentrations favor nucleation and compact clustering. These findings unify a range of biological phenomena - such as chemotaxis, tissue remodeling, and self-generated gradient navigation - within a simple, physically grounded framework. Our results also offer insights into designing artificial systems with emergent collective behavior, including robotic swarms or synthetic active matter. |
| title | Self-organized fractal architectures driven by motility-dependent chemotactic feedback |
| topic | Biological Physics Soft Condensed Matter Statistical Mechanics |
| url | https://arxiv.org/abs/2504.16539 |