A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions and evaluate the pathway reconstructions when applied to six diverse signaling pathways from the NetPath database. The optimal DAGs outperform an existing k-shortest paths method for pathway reconstruction, and the new reconstructions are enriched for different biological processes. Growing DAGs is a promising step toward reconstructing pathways that provably optimize a specific cost function.

中文翻译：

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增长有向无环图：路径重建算法的优化函数。

分子系统生物学的一个主要挑战是了解蛋白质如何传递外部信号以改变基因表达。通过蛋白质相互作用网络计算重建这些信号通路可以帮助了解现有通路数据库中缺失的内容。我们提出了一个新的路径重建问题，即从蛋白质相互作用网络中的一组起始蛋白质迭代地生成有向无环图（DAG）。我们提出了一种算法，可证明返回两个不同成本函数的最佳 DAG，并在应用于 NetPath 数据库中的六种不同信号通路时评估通路重建。最优 DAG 优于现有的用于路径重建的 k 最短路径方法，并且新的重建针对不同的生物过程进行了丰富。