Abstract

BackgroundFunctional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks.ResultsIn this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes.ConclusionsThrough extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Highlights

  • Functional modules in biological networks consist of numerous biomolecules and their complicated interactions

  • As it has been proved that functional modules or signaling pathways are often well conserved across different biological networks [4, 5], comparative network analysis has been emerging computational means to identify and predict conserved functional modules in different biological networks [6]

  • We propose a heuristic network querying algorithm based on the CUFID framework and a seed-and-extension approach

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Summary

Introduction

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In addition to investigating functions of an individual protein, taking a set of proteins and their interactions into account is significantly effective to identify novel functions of proteins because particular protein-protein interactions (PPI) can inhibit or promote a certain biological process These proteins and their interactions can form a functional module that performs a particular biological function, and identifying functional modules is necessary to understand core biological mechanisms in a cell and it can be utilized to design a novel drug, effective diagnosis, and therapy for complex disease such as cancer [1,2,3]. As it has been proved that functional modules or signaling pathways are often well conserved across different biological networks [4, 5], comparative network analysis has been emerging computational means to identify and predict conserved functional modules in different biological networks [6]

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