蛋白质信号网络的增广稀疏重构
Augmented Sparse Reconstruction of Protein Signaling Networks
摘要：
细胞内蛋白质信号和生化网络的重建和识别是当今生物学中至关重要的问题。我们试图开发一种数学方法来解决这个问题，作为一个测试案例，其中一个最充分研究和临床上重要的信号网络在生物学今天，表皮生长因子受体（EGFR）驱动的信号级联。更具体地说，我们提出了一种方法，增强稀疏重建，从一个小的轨迹集不同的初始条件的常微分方程（ODE）网络的节点之间的链接识别。我们的方法通过使用所有给定轨迹的积分的集合和通过衰减表示本身中的项块来构建表示系统。然后用随机向量扩充表示系统，并使用1-范数的最小化来找到每个节点的动态相互作用的稀疏表示。随机向量的增强是至关重要的，因为稀疏性本身无法处理表示中的大误差变量。增强稀疏重建允许考虑潜在的非常大的模型空间，并且即使在测量的轨迹中添加了适度的噪声时，也能够高精度地检测节点之间的少数相关链接。在展示了我们的方法在EGFR蛋白网络模型上的性能后，我们简要地概述了这种方法未来潜在的治疗应用。
英文摘要：
The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology today. We sought to develop a mathematical approach to this problem using, as a test case, one of the most well-studied and clinically important signaling networks in biology today, the epidermal growth factor receptor (EGFR) driven signaling cascade. More specifically, we suggest a method, augmented sparse reconstruction, for the identification of links among nodes of ordinary differential equation (ODE) networks from a small set of trajectories with different initial conditions. Our method builds a system of representation by using a collection of integrals of all given trajectories and by attenuating block of terms in the representation itself. The system of representation is then augmented with random vectors, and minimization of the 1-norm is used to find sparse representations for the dynamical interactions of each node. Augmentation by random vectors is crucial, since sparsity alone is not able to handle the large error-in-variables in the representation. Augmented sparse reconstruction allows to consider potentially very large spaces of models and it is able to detect with high accuracy the few relevant links among nodes, even when moderate noise is added to the measured trajectories. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.
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