2026-09-15 東京大学
スケールフリーネットワークと均質ネットワーク上における理論モデルの細胞成長率
＜関連情報＞
- https://www.c.u-tokyo.ac.jp/info/news/topics/20260915140000.html
- https://www.pnas.org/doi/10.1073/pnas.2614238123
高密度スケールフリー代謝ネットワークにおける飢餓抑制：触媒反応ネットワークの動的平均場解析 Starvation suppression in dense scale-free metabolic networks: Dynamical mean-field analysis of catalytic reaction networks
Kota Mitsumoto and Shuji Ishihara
Proceedings of the National Academy of Sciences Published:September 10, 2026
DOI:https://doi.org/10.1073/pnas.2614238123
Abstract
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 a power-law in-degree distribution of the underlying catalytic reaction network gives rise to a power-law distribution of biomolecular abundances with the same exponent. Large-scale numerical simulations validate these predictions. Our results provide a theoretical framework linking network topology and metabolic dynamics.
