Direct Lyapunov exponent analysis enables parametric study of transient signalling governing cell behaviour
Direct Lyapunov exponent analysis enables parametric study of transient signalling governing cell behaviour
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DOI：
10.1049/ip-syb:20050065
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发表时间：
2006-11-01
期刊：
影响因子：
--
通讯作者：
Lauffenburger, D. A.
中科院分区：
文献类型：
--
作者：
Aldridge, B. B.;Haller, G.;Lauffenburger, D. A.
计算模型有助于定量了解细胞信号网络。一个重要的目标是确定多个网络组件如何一起工作来管理细胞响应，即确定细胞的“信号-响应”关系。在基于微分方程的模型的背景下，有几种方法可以研究稳态信号。然而，许多生物网络通过在瞬时激活状态期间操作的时变信号来影响细胞行为，所述瞬时激活状态最终返回到基础稳态。一种计算方法，从动力系统分析，以辨别不同的瞬态信号与替代细胞命运。直接有限时间李雅普诺夫指数（DLE）识别相空间域的初始条件的高灵敏度。这些域描绘了表现出定性不同的瞬态活动的区域，这些活动使用稳态分析无法区分，但对应于不同的结果。这些方法被应用到一个物理化学模型的分子之间的相互作用，半胱天冬酶-3，半胱天冬酶-8和X-连锁抑制剂的细胞凋亡蛋白质，其瞬时激活决定细胞死亡对生存的命运。DLE分析能够识别一个分界线，通过定义导致凋亡细胞死亡的初始条件来定量表征网络行为。预计DLE分析将有助于在更大的信号网络模型中对表型结果进行理论研究。
Computational models aid in the quantitative understanding of cell signalling networks. One important goal is to ascertain how multiple network components work together to govern cellular responses, that is, to determine cell 'signal-response' relationships. Several methods exist to study steady-state signals in the context of differential equation-based models. However, many biological networks influence cell behaviour through time-varying signals operating during a transient activated state that ultimately returns to a basal steady-state. A computational approach adapted from dynamical systems analysis to discern how diverse transient signals relate to alternative cell fates is described. Direct finite-time Lyapunov exponents (DLEs) are employed to identify phase-space domains of high sensitivity to initial conditions. These domains delineate regions exhibiting qualitatively different transient activities that would be indistinguishable using steady-state analysis but which correspond to different outcomes. These methods are applied to a physicochemical model of molecular interactions among caspase-3, caspase-8 and X-linked inhibitor of apoptosis - proteins whose transient activation determines cell death against survival fates. DLE analysis enabled identification of a separatrix that quantitatively characterises network behaviour by defining initial conditions leading to apoptotic cell death. It is anticipated that DLE analysis will facilitate theoretical investigation of phenotypic outcomes in larger models of signalling networks.