通过信念传播发现细胞信号转导中未检测到的蛋白质关联
Finding undetected protein associations in cell signaling by belief propagation
摘要：
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。高通量实验确定了许多分子组成的级联反应，但可能通过未知的合作伙伴相互作用。它们中的一些可以使用来自蛋白质相互作用网络和mRNA表达谱的整合的数据来检测。这个推理问题可以映射到由这些数据集定义的网络中找到合适的最佳连通子图的问题。优化过程在一般情况下是计算上难以处理的。在这里，我们提出了一个新的分布式算法，为这项任务，从统计物理学的启发，并将此计划的α因子和药物扰动数据在酵母。我们确定了COS8蛋白的作用，这是一个以前未知功能的基因家族的成员，并通过遗传实验验证了结果。我们提出的算法是特别适合于非常大的数据集，可以并行运行，并可以适应其他问题的系统生物学。在著名的基准测试中，它优于该领域的其他算法。
英文摘要：
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.
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