A Computational Analysis of the Dynamic Roles of Talin, Dok1, and PIPKI for Integrin Activation

Integrin signaling regulates cell migration and plays a pivotal role in developmental processes and cancer metastasis. Integrin signaling has been studied extensively and much data is available on pathway components and interactions. Yet the data is fragmented and an integrated model is missing. We use a rule-based modeling approach to integrate available data and test biological hypotheses regarding the role of talin, Dok1 and PIPKI in integrin activation. The detailed biochemical characterization of integrin signaling provides us with measured values for most of the kinetics parameters. However, measurements are not fully accurate and the cellular concentrations of signaling proteins are largely unknown and expected to vary substantially across different cellular conditions. By sampling model behaviors over the physiologically realistic parameter range we find that the model exhibits only two different qualitative behaviors and these depend mainly on the relative protein concentrations, which offers a powerful point of control to the cell. Our study highlights the necessity to characterize model behavior not for a single parameter optimum, but to identify parameter sets that characterize different signaling modes.

整合素（Integrin）信号通路可调控细胞迁移，并在发育过程与癌症转移中发挥关键作用。目前针对整合素信号通路的研究已较为充分，关于通路组分及其相互作用的数据集也较为丰富。但现有数据较为零散，缺乏统一的整合模型。本研究采用基于规则的建模方法整合现有数据，并验证关于踝蛋白（talin）、Dok1与PIPKI在整合素激活中作用的生物学假说。针对整合素信号通路的详尽生化表征，为我们提供了多数动力学参数的实测值。然而，实测数据并非完全准确，且信号蛋白的细胞内浓度大多尚未明确，同时预计其在不同细胞环境中会存在显著差异。通过在生理合理的参数范围内对模型行为进行采样分析，我们发现该模型仅表现出两种不同的定性行为，且这两种行为主要取决于蛋白的相对浓度，这为细胞提供了一个强有力的调控节点。本研究强调，无需针对单一参数最优值来表征模型行为，而是应通过识别不同参数集来刻画不同的信号通路模式。