Finding undetected protein associations in cell signaling by belief propagation
Finding undetected protein associations in cell signaling by belief propagation
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DOI：
10.1073/pnas.1004751108
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发表时间：
2011-01-11
影响因子：
11.1
通讯作者：
Zecchina, R.
中科院分区：
文献类型：
--
作者：
Bailly-Bechet, M.;Borgs, C.;Zecchina, R.
外部信息主要通过信号级联和转录激活在细胞中传播，使其能够对广泛的环境变化做出反应。高通量实验确定了此类级联的许多分子成分，但这些分子成分可能通过未知的伙伴相互作用。其中一些可以使用来自蛋白质-蛋白质相互作用网络和 mRNA 表达谱整合的数据来检测。该推理问题可以映射到寻找由这些数据集定义的网络的适当最优连通子图的问题。事实证明，优化过程通常在计算上很困难。在这里，我们受统计物理学的启发，提出了一种用于此任务的新分布式算法，并将该方案应用于酵母中的 α 因子和药物扰动数据。我们确定了 COS8 蛋白（功能先前未知的基因家族成员）的作用，并通过基因实验验证了结果。我们提出的算法特别适合非常大的数据集，可以并行运行，并且可以适应系统生物学中的其他问题。在著名的基准测试中，它的性能优于该领域的其他算法。
External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.