Starvation suppression in scale-free metabolic networks: Dynamical mean-field analysis of dense catalytic reaction networks
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915877493932032 |
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| author | Mitsumoto, Kota Ishihara, Shuji |
| author_facet | Mitsumoto, Kota Ishihara, Shuji |
| contents | Cellular metabolic networks exhibit scale-free topologies with power-law degree distributions across diverse organisms. Although such topologies are often linked to mutational robustness and evolutionary advantage, their role in metabolic dynamics remains unclear. Using dynamical mean-field theory, we derive an exact solution for an intracellular catalytic reaction model on dense random networks with arbitrary degree distributions. We show that the metabolic-starvation transition observed under nutrient-poor conditions for homogeneous degree distributions disappears when the out-degree distribution is scale-free. We also show that the power-law distribution of biomolecular abundances observed in real cells reflects the power-law in-degree distribution of the underlying catalytic reaction network. Large-scale numerical simulations validate these predictions. Our results provide a theoretical framework linking network topology and metabolic dynamics, and identify a dynamical advantage of scale-free topology under nutrient limitation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19850 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Starvation suppression in scale-free metabolic networks: Dynamical mean-field analysis of dense catalytic reaction networks Mitsumoto, Kota Ishihara, Shuji Statistical Mechanics Disordered Systems and Neural Networks Soft Condensed Matter Adaptation and Self-Organizing Systems Biological Physics Cellular metabolic networks exhibit scale-free topologies with power-law degree distributions across diverse organisms. Although such topologies are often linked to mutational robustness and evolutionary advantage, their role in metabolic dynamics remains unclear. Using dynamical mean-field theory, we derive an exact solution for an intracellular catalytic reaction model on dense random networks with arbitrary degree distributions. We show that the metabolic-starvation transition observed under nutrient-poor conditions for homogeneous degree distributions disappears when the out-degree distribution is scale-free. We also show that the power-law distribution of biomolecular abundances observed in real cells reflects the power-law in-degree distribution of the underlying catalytic reaction network. Large-scale numerical simulations validate these predictions. Our results provide a theoretical framework linking network topology and metabolic dynamics, and identify a dynamical advantage of scale-free topology under nutrient limitation. |
| title | Starvation suppression in scale-free metabolic networks: Dynamical mean-field analysis of dense catalytic reaction networks |
| topic | Statistical Mechanics Disordered Systems and Neural Networks Soft Condensed Matter Adaptation and Self-Organizing Systems Biological Physics |
| url | https://arxiv.org/abs/2603.19850 |