MOTIVATION: Target characterization for a biochemical network is a heuristic evaluation process that produces a characterization model that may aid in predicting the suitability of each molecule for drug targeting. These approaches are typically used in drug research to identify novel potential targets using insights from known targets. Traditional approaches that characterize targets based on their molecular characteristics and biological function require extensive experimental study of each protein and are infeasible for evaluating larger networks with poorly understood proteins. Moreover, they fail to exploit network connectivity information which is now available from systems biology methods. Adopting a network-based approach by characterizing targets using network features provides greater insights that complement these traditional techniques. To this end, we present Tenet (Target charactErization using NEtwork Topology), a network-based approach that characterizes known targets in signalling networks using topological features. RESULTS: Tenet first computes a set of topological features and then leverages a support vector machine-based approach to identify predictive topological features that characterizes known targets. A characterization model is generated and it specifies which topological features are important for discriminating the targets and how these features should be combined to quantify the likelihood of a node being a target. We empirically study the performance of Tenet from a wide variety of aspects, using several signalling networks from BioModels with real-world curated outcomes. Results demonstrate its effectiveness and superiority in comparison to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: Our software is available freely for non-commercial purposes from: https://sites.google.com/site/cosbyntu/softwares/tenet CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Given the growth of large-scale protein-protein interaction (PPI) networks obtained across multiple species and conditions, network alignment is now an important research problem. Network alignment performs comparative analysis across multiple PPI networks to understand their connections and relationships. However, PPI data in high-throughput experiments still suffer from significant false-positive and false-negatives rates. Consequently, high-confidence network alignment across entire PPI networks is not possible. At best, local network alignment attempts to alleviate this problem by completely ignoring low-confidence mappings; global network alignment, on the other hand, pairs all proteins regardless. To this end, we propose an alternative strategy: instead of full alignment across the entire network or completely ignoring low-confidence regions, we aim to perform highly specific protein-to-protein alignments where data confidence is high, and fall back on broader functional region-to-region alignment where detailed protein-protein alignment cannot be ascertained. The basic idea is to provide an alignment of multiple granularities to allow biological predictions at varying specificity. RESULTS: DualAligner performs dual network alignment, in which both region-to-region alignment, where whole subgraph of one network is aligned to subgraph of another, and protein-to-protein alignment, where individual proteins in networks are aligned to one another, are performed to achieve higher accuracy network alignments. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data. We tested DualAligner on the global networks from IntAct and demonstrated the superiority of our approach compared with state-of-the-art network alignment methods. We studied the effects of parameters in DualAligner in controlling the quality of the alignment. We also performed a case study that illustrates the utility of our approach. AVAILABILITY AND IMPLEMENTATION: http://www.cais.ntu.edu.sg/∼assourav/DualAligner/.

MOTIVATION: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. RESULTS: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. SUPPLEMENTARY INFORMATION: Supplementary data are available at the Bioinformatics online. AVAILABILITY: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

BACKGROUND: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint. RESULTS: We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI. CONCLUSION: By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

BACKGROUND: The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems. METHODS: Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets. RESULTS: OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared.

Developing complex models of cellular function requires the collaboration of multiple teams of researchers remotely distributed worldwide. A challenge of computational systems biology is to find easy and accessible mechanisms to enable such collaboration to construct higher level models of cellular function. This paper presents the development of an on-line Web portal for enabling open access to Cytosolve, an existing, proven and scalable computational architecture for integrating quantitative molecular pathways. The developed graphical user interface allows ease-of-use for developers of quantitative molecular pathway models to remotely collaborate to build larger and more complex models using the Cytosolve infrastructure. The on-line Web portal will be accessible and it will allow users to remotely collaborate with the existing Cytosolve computational environment that supports integration of models in a parallel manner without geographical restrictions. A creator of a model will be able to integrate their model from their local location to an ensemble of distributed models through this on-line Web portal.