De la Fuente I M, Benitez N, Santamaria A, Aguirregabiria J M, Veguillas J
Department of Cell Biology and Morphological Sciences, School of Medicine, University of the Basque Country, 48940 Leioa, Vizcaya, Spain.
Bull Math Biol. 1999 May;61(3):573-95. doi: 10.1006/bulm.1999.0103.
In an attempt to improve the understanding of complex metabolic dynamic phenomena, we have analysed several 'metabolic networks', dynamical systems which, under a single formulation, take into account the activity of several catalytic dissipative structures, interconnected by substrate fluxes and regulatory signals. These metabolic networks exhibit a rich variety of self-organized dynamic patterns, with e.g., phase transitions emerging in the whole activity of each network. We apply Hurst's R/S analysis to several time series generated by these metabolic networks, and measure Hurst exponents H < 0.5 in most cases. This value of H, indicative of antipersistent processes, is detected at very high significance levels, estimated with detailed Monte Carlo simulations. These results show clearly the considered type of metabolic networks exhibit long-term memory phenomena.
为了更好地理解复杂的代谢动态现象，我们分析了几个“代谢网络”，即动力学系统，在单一公式下，这些系统考虑了由底物通量和调节信号相互连接的几个催化耗散结构的活性。这些代谢网络展现出丰富多样的自组织动态模式，例如，每个网络的整体活性中会出现相变。我们将赫斯特的重标极差分析应用于这些代谢网络生成的几个时间序列，并在大多数情况下测得赫斯特指数H<0.5。通过详细的蒙特卡罗模拟估计，H的这个值表明存在反持续过程，且具有非常高的显著性水平。这些结果清楚地表明，所考虑的这类代谢网络呈现出长期记忆现象。
