代謝ダイナミクスの応答性とネットワーク構造の関係に新たな知見

2025-05-20 東京大学

代謝ネットワークの微分方程式モデルを用いた応答性解析

<関連情報>

• https://www.s.u-tokyo.ac.jp/ja/press/10791/
• https://elifesciences.org/articles/98800

代謝ダイナミクスの摂動-応答解析から、補酵素にハードコードされた応答性とネットワークの疎密が明らかになった Perturbation-response analysis of in silico metabolic dynamics revealed hard-coded responsiveness in the cofactors and network sparsity

Yusuke Himeoka ,Chikara Furusawa
eLife  Published:May 20, 2025
DOI:https://doi.org/10.7554/eLife.98800.4

Abstract

Homeostasis is a fundamental characteristic of living systems. Unlike rigidity, homeostasis necessitates that systems respond flexibly to diverse environments. Understanding the dynamics of biochemical systems when subjected to perturbations is essential for the development of a quantitative theory of homeostasis. In this study, we analyze the response of bacterial metabolism to externally imposed perturbations using kinetic models of Escherichia coli’s central carbon metabolism in nonlinear regimes. We found that three distinct kinetic models consistently display strong responses to perturbations; in the strong responses, minor initial discrepancies in metabolite concentrations from steady-state values amplify over time, resulting in significant deviations. This pronounced responsiveness is a characteristic feature of metabolic dynamics, especially since such strong responses are seldom seen in toy models of the metabolic network. Subsequent numerical studies show that adenyl cofactors consistently influence the responsiveness of the metabolic systems across models. Additionally, we examine the impact of network structure on metabolic dynamics, demonstrating that as the metabolic network becomes denser, the perturbation response diminishes—a trend observed commonly in the models. To confirm the significance of cofactors and network structure, we constructed a simplified metabolic network model underscoring their importance. By identifying the structural determinants of responsiveness, our findings offer implications for bacterial physiology, the evolution of metabolic networks, and the design principles for robust artificial metabolism in synthetic biology and bioengineering.