IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul-Aug;15(4):1079-1091. doi: 10.1109/TCBB.2017.2710129.
Signaling networks are involved in almost all major diseases such as cancer. As a result of this, understanding how signaling networks function is vital for finding new treatments for many diseases. Using gene knockdown assays such as RNA interference (RNAi) technology, many genes involved in these networks can be identified. However, determining the interactions between these genes in the signaling networks using only experimental techniques is very challenging, as performing extensive experiments is very expensive and sometimes, even impractical. Construction of signaling networks from RNAi data using computational techniques have been proposed as an alternative way to solve this challenging problem. However, the earlier approaches are either not scalable to large scale networks, or their accuracy levels are not satisfactory. In this study, we integrate RNAi data given on a target network with multiple reference signaling networks and phylogenetic trees to construct the topology of the target signaling network. In our work, the network construction is considered as finding the minimum number of edit operations on given multiple reference networks, in which their contributions are weighted by their phylogenetic distances to the target network. The edit operations on the reference networks lead to a target network that satisfies the RNAi knockdown observations. Here, we propose two new reference-based signaling network construction methods that provide optimal results and scale well to large-scale signaling networks of hundreds of components. We compare the performance of these approaches to the state-of-the-art reference-based network construction method SiNeC on synthetic, semi-synthetic, and real datasets. Our analyses show that the proposed methods outperform SiNeC method in terms of accuracy. Furthermore, we show that our methods function well even if evolutionarily distant reference networks are used. Application of our methods to the Apoptosis and Wnt signaling pathways recovers the known protein-protein interactions and suggests additional relevant interactions that can be tested experimentally.
信号网络几乎涉及所有重大疾病，如癌症。因此，了解信号网络如何运作对于寻找许多疾病的新治疗方法至关重要。通过基因敲低测定，如 RNA 干扰（RNAi）技术，可以鉴定许多涉及这些网络的基因。然而，仅使用实验技术确定信号网络中这些基因之间的相互作用是非常具有挑战性的，因为进行广泛的实验非常昂贵，有时甚至不切实际。使用计算技术从 RNAi 数据构建信号网络已被提出作为解决这一具有挑战性问题的替代方法。然而，早期的方法要么不适用于大规模网络，要么其准确性水平不理想。在这项研究中，我们将目标网络上的 RNAi 数据与多个参考信号网络和系统发育树集成，以构建目标信号网络的拓扑结构。在我们的工作中，网络构建被认为是在给定的多个参考网络中找到编辑操作的最小数量，其中它们的贡献通过它们与目标网络的系统发育距离进行加权。对参考网络的编辑操作导致满足 RNAi 敲低观察的目标网络。在这里，我们提出了两种新的基于参考的信号网络构建方法，它们提供了最佳结果，并很好地扩展到具有数百个组件的大规模信号网络。我们将这些方法的性能与基于 SiNeC 的最先进的基于参考的网络构建方法进行比较，包括合成、半合成和真实数据集。我们的分析表明，在所提出的方法在准确性方面优于 SiNeC 方法。此外，我们还表明，即使使用进化上遥远的参考网络，我们的方法也能很好地发挥作用。将我们的方法应用于凋亡和 Wnt 信号通路，恢复了已知的蛋白质-蛋白质相互作用，并提出了可以通过实验测试的其他相关相互作用。
