STON: exploring biological pathways using the SBGN standard and graph databases

BackgroundWhen modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.ResultsWe present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.ConclusionSTON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.

[1]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[2]  Ferran Reverter,et al.  Kernel-PCA data integration with enhanced interpretability , 2014, BMC Systems Biology.

[3]  Taesung Park,et al.  Phenotype prediction from genome-wide association studies: application to smoking behaviors , 2012, BMC Systems Biology.

[4]  Giorgio Valentini,et al.  Scalable Network-based Learning Methods for Automated Function Prediction based on the Neo 4 j Graph-database , 2013 .

[5]  Yixin Chen,et al.  A comparison of a graph database and a relational database: a data provenance perspective , 2010, ACM SE '10.

[6]  Yukiko Matsuoka,et al.  Software support for SBGN maps: SBGN-ML and LibSBGN , 2012, Bioinform..

[7]  Gary D Bader,et al.  How to visually interpret biological data using networks , 2009, Nature Biotechnology.

[8]  Glen E. P. Ropella,et al.  Toward modular biological models: defining analog modules based on referent physiological mechanisms , 2014, BMC Systems Biology.

[9]  Samik Ghosh,et al.  AlzPathway: a comprehensive map of signaling pathways of Alzheimer’s disease , 2012, BMC Systems Biology.

[10]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[11]  Piero Fariselli,et al.  Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure , 2011, BioData Mining.

[12]  Gary D. Bader,et al.  Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative , 2015, Front. Bioeng. Biotechnol..

[13]  E. Barillot,et al.  Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps , 2015, Oncogenesis.

[14]  Carlos González-Alcón,et al.  Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies , 2012, BMC Systems Biology.

[15]  Nicolas Le Novère,et al.  Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2 , 2015, Journal of integrative bioinformatics.

[16]  Matthias Klapperstück,et al.  VANTED v2: a framework for systems biology applications , 2012, BMC Systems Biology.

[17]  A. Barabasi,et al.  A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. , 2015, Human molecular genetics.

[18]  Olaf Wolkenhauer,et al.  Challenges and opportunities for system biology standards and tools in medical research , 2016, ODLS.

[19]  Olaf Wolkenhauer,et al.  Combining computational models, semantic annotations and simulation experiments in a graph database , 2015, Database J. Biol. Databases Curation.

[20]  Samik Ghosh,et al.  Integrating Pathways of Parkinson's Disease in a Molecular Interaction Map , 2013, Molecular Neurobiology.

[21]  Falk Schreiber,et al.  Editing, validating and translating of SBGN maps , 2010, Bioinform..

[22]  Astrid Junker,et al.  Wiring diagrams in biology: towards the standardized representation of biological information. , 2012, Trends in biotechnology.

[23]  Anthony J. Connor,et al.  Semantically Linking In Silico Cancer Models article openly Please share how this access benefits you. Your story matters , 2014 .

[24]  Stefan Wiemann,et al.  KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor , 2009, Bioinform..

[25]  Emek Demir,et al.  Algorithms for effective querying of compound graph-based pathway databases , 2009, BMC Bioinformatics.

[26]  Christopher J. Rawlings,et al.  Representing and querying disease networks using graph databases , 2016, BioData Mining.

[27]  René Peinl,et al.  Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j , 2013, EDBT '13.

[28]  Reinhard Schneider,et al.  Using graph theory to analyze biological networks , 2011, BioData Mining.

[29]  N. Kikuchi,et al.  CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks , 2008, Proceedings of the IEEE.

[30]  Lars Juhl Jensen,et al.  Are graph databases ready for bioinformatics? , 2013, Bioinform..

[31]  Keiichiro Ono,et al.  cyNeo4j: connecting Neo4j and Cytoscape , 2015, Bioinform..