Pairwise Alignment of Protein Interaction Networks

Abstract

With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational cost.

Phương Pháp Tối Ưu Đàn Kiến Dóng Hàng Toàn Cục Các Mạng Tương Tác Protein

“…Tuy nhiên, chỉ tính tƣơng đồng trình tự thƣờng không đủ để nhận dạng các phức hợp protein đƣợc bảo tồn [12,24,26]. Sự phát triển của các kỹ thuật công nghệ sinh học trong hơn thập kỷ qua đã cho phép xây dựng đƣợc các mạng tƣơng tác protein (Protein-Protein Interraction Network -PPI Network) cho nhiều loài sinh vật.…”

A Survey on Evolutionary Analysis in PPI Networks

“…For instance, Koyutürk, Kim, Topkara, Subramaniam, Grama & Szpankowski (2006) partitions the alignment graph into smaller clusters by performing an approximated balanced ratio-cut. In another method by the most frequent interaction motifs are extracted from an orthology-contracted graph.…”

Frequent Pattern Discovery in Multiple Biological Networks: Patterns and Algorithms

“…A plenty of data mining algorithms [19] have been developed by employing different search strategies, such as A priori-based approach and pattern growth. (2) "Multiple network alignment in a small number of massive networks" focuses on comparing biological networks from different species [14,[16][17][18]27]. Its task is to extract conserved subgraphs across networks of multiple species, where the nodes in different networks can have a many-to-many mapping based on the orthologous gene relationships, thus involving the "subgraph isomorphism" problem.…”