Visualizing multi-omics data on models

<p>Biological pathways provide intuitive frameworks to integrate and co-analyze different kinds of biological data, such as system-wide transcriptomic, proteomic, and metabolomic measurements. While insightful, pathway analysis is generally limited to qualitative conclusions, and the analyses can only be as powerful as the curated annotations can enable. Using our open-source pathway analysis platform, PathVisio, we bridge pathway analysis to the wealth of quantitative approaches already in development for metabolic network modeling, such as flux balance analysis and dynamic simulation. Our focus is on the visualization of the modeling results, which will be critical for understanding how simulated models correlate with experimental measurements.<br>The same biological processes that are visualized in pathways are also described by quantitative models. For example, the arrows that connect entities within metabolic pathways actually represent metabolite fluxes. The integration of large scale data analysis with modeled or measured fluxomics data, will help to gain more insights into the mechanism of the biological process.<br>PathVisio[1] is an open source pathway editing and analysis tool. It can be used to draw biological pathways as graphical diagrams, nodes of which denoted the genes, proteins, and metabolites and the edges linking the nodes denote the relationship between them. The genes, proteins, and metabolites in the pathway can be annotated with database identifiers linking them to online resources. This also allows experimental data (e.g. microarray data) to be mapped and visualized on the pathway diagram . The PathVisio core software has been extended as part of this project to allow annotation of interactions in a biological pathway. This would allow linking interactions to online data sources as well as visualization of data on them.</p> <p>PathVisio uses BridgeDb[2], an identifier mapping framework for biological applications, to allow the translation of identifiers between different systems. We have created a BridgeDb interaction database using mappings between interaction identifiers obtained from Rhea [3]. Due to the transitivity feature in bridgedb, mapping databases used can be clubbed, we plan to develop a second interaction database using mappings from ConsensusPathdb [4], we have an ongoing collaboration with the ConsensusPathDb team. This will allow users to define interactions using most common interaction identifier systems.</p> <p>PathVisio uses GPML[5] to store and exchange pathways. While mathematical models are commonly described in SBML [6], a standard format widely accepted for storing and exchanging mathematical models. We developed an SBML importer plugin for PathVisio in order to facilitate model curation and extension by providing modelers with an up-to-date graphical (SBGN-PD) representation of their model. This representation can then be compared to existing pathways for the same process, which will facilitate both pathway improvement and critical assessment of model implementation aspects like lumping of reaction steps and reduction of parallel routes. The plugin also enables model validation and direct import of SBML models from Biomodels. Furthermore, imported models can then be shared with the community for distribution and further curation through WikiPathways (wikipathways.org), which also uses GPML as its native format.<br>We have also developed a PathVisio plugin for visualizing data on interactions, e.g flux data on biological pathways. This along with existing PathVisio functionality enables model outcomes to be graphically visualized on the model representation to ease comprehension.<br>Visualization of cutting-edge models, including their outputs, have traditionally lagged behind development of the models themselves. As such, the proposed integrated visualization will have several important impacts on the field:<br>makes modeling results immediately more accessible and interpretable to biologists who wish to learn from them<br>enables researchers who develop flux modeling approaches to better improve and distribute their models<br>facilitates new ways to explore effects on mechanism, such as genetic effects. In our group, we are planning to use pathways to link copy number variations and polymorphisms in genes to their biological consequences. These changes will often affect model kinetics, so by integrating flux modeling with pathways, we can use models to evaluate which of the observed genetic changes will significantly alter metabolism.</p> <p>References:<br>1) Presenting and exploring biological pathways with PathVisio. van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin BR, Evelo C. BMC Bioinformatics 2008, 9:399 (25 Sep 2008)<br>2)The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services. van Iersel MP, Pico AR, Kelder T, Gao J, Ho I, Hanspers K, Conklin BR, Evelo CT. BMC Bioinformatics. 2010 Jan 4;11(1):5.<br>3) Rhea - a manually curated resource of biochemical reactions. Rafael Alcántara, Kristian B. Axelsen, Anne Morgat, Eugeni Belda, Elisabeth Coudert, Alan Bridge, Hong Cao, Paula de Matos, Marcus Ennis, Steve Turner, Gareth Owen, Lydie Bougueleret, Ioannis Xenarios, Christoph Steinbeck, Nucleic Acids Research (2012) 40:D754-D760; doi: 10.1093/nar/gkr1126<br>4) The ConsensusPathDB interaction database: 2013 update. Kamburov, A. et al. Nucleic Acids Res. ( 2013)41 (D1): D793-D800.doi: 10.1093/nar/gks1055<br>5) http://developers.pathvisio.org/wiki/EverythingGpml<br>6)The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. M. Hucka, A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, and the rest of the SBML Forum , Bioinformatics (2003) 19 (4): 524-531 doi:10.1093/bioinformatics/btg015<br>7) WikiPathways: building research communities on biological pathways. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo C, Pico AR., Nucleic Acids Resl. 2012 Jan;40(1):D1301-7.