Using biological network approaches for dynamic extension of micronutrient related pathways with regulatory information.

Editor's Notes

• #3: Probably not an iPAD, those microarrays were at least 10 years old.
• #6: A closer look at the same pathway.Note that this uses MIM notation from the MIM PathVisio plugin.In general the connections between different genes and metabolites describe the network underlying the pathway. Note that this is already quite complex since there are different ways to show what interacts with what.Graphical methods to capture this like MIM and SBGN definitely help. The result can be captures in descriptive relationships in BioPax,
• #9: Probably not an iPAD, those microarrays were at least 10 years old.
• #15: As soon as you have entered one (and only one) identifier to describe what gene product or metabolite you really mean this information is linked to many other identifiers from other databases and links to these respective pages are shown in the so called “backpage” (actually one of the pages under the tabs at the righthand side of the pathway).
• #16: As soon as you have entered one (and only one) identifier to describe what gene product or metabolite you really mean this information is linked to many other identifiers from other databases and links to these respective pages are shown in the so called “backpage” (actually one of the pages under the tabs at the righthand side of the pathway).
• #17: BridgeDB (see www.bridgedb.org and the paper mentioned on the slide) provides the mechanism needed for that identifier mapping.
• #18: There are just too many SNPs for any given gene.
• #19: An overview of the Open Phacts project that pulls in lots of information in a semantic web triple store (including information from WikiPathways RDF) and then provides that for use in other tools. In WikiPathways we use that to suggest possible pathway extensions to curators
• #21: Probably not an iPAD, those microarrays were at least 10 years old.
• #22: Introducing a problem
• #23: And a solution that isn’t really a solution. There are just too many things you could add.
• #24: The PathVisio Regulatory Interaction plugin (author Stefan van Helden) has a new approach where information is not really added to a pathway, but shown in a separate page upon request.
• #25: Probably not an iPAD, those microarrays were at least 10 years old.
• #30: The approach takes into account all data use (pathways, interactions and experimentally determined weight). Check out the original paper for details.
• #31: Example result. Pathways with stronger interaction based on gene snot present in them.
• #32: And you can do the same for relatively large sets of pathways “driving” a process like apoptosis.
• #33: CyTargetLinker is a Cytoscape plugin that can be used to extend one network with information about things targeting entities in that network from databases that are created as a network. It already provides a number of target relation databases as mentioned on the slide.
• #34: Example of a target network. (You will normally see this, it contains the information that is used to extend your source network).
• #36: You can drive it from a gene set, that isn’t even a network at the start. But when miRNAs are found to target more than one gene in the ggroup the network is created on the fly.
• #37: Or you can bootstrap the approach from an existing network. Which can be a pathway based one imported with the GPML plugin like shown here.
• #38: There are just too many SNPs for any given gene.
• #39: There are loads of bioinformatics tools out there (like Sift and Polyphen) that allow us to estimate functional effects of SNPs on coded protein (activity or protein-protein interactions), binding site for transcription factors in the DNA, or miRNA in RNA. Doing that we can decide what edges SNPs would affect (and how much in what direction). Now as soon as you do that you can use the result to strengthen SNP statistics (ie create groups that can be used for supervised types of group based GWAS analysis) or to build predictive models to estimate that specific (personal or tissue/tumor based) sets of variations would do. That provides a need to use the pathways to link experimental (genomics) data not only to the genetic variations occurring in there, but also to modeling results
• #40: Showing the concept. Integrating flux predictions from modelling (of course that could also be real fluxomics data)
• #42: Many people involved in this work. (Really many if you count associated groups like the plugin developers, pathway curators etc).Most importantSF group (Kristina Hanspers, Bruce Conklin and Alex Pico) collaborating on many things but primarily WikiPatwhaysMartijn van Iersel top left (PathVisio, BridgeDB). Thomas Kelder (top middle) (WikiPathways including webservices, pathway integration networks for nutrigenomics), Martina Kutmon (top right) (CyTargetLinker, PathVisio further development), Andra Waagmeester (second row, right) (WikiPathways RDF), Anwesha Dutta (bottom, 2nd from the left) (flux visualization), Stefan van Helden (not on the picture) for the RI PathVisio plugin
• #43: Many people involved in this work. (Really many if you count associated groups like the plugin developers, pathway curators etc).Most importantSF group (Kristina Hanspers, Bruce Conklin and Alex Pico) collaborating on many things but primarily WikiPatwhaysMartijn van Iersel top left (PathVisio, BridgeDB). Thomas Kelder (top middle) (WikiPathways including webservices, pathway integration networks for nutrigenomics), Martina Kutmon (top right) (CyTargetLinker, PathVisio further development), Andra Waagmeester (second row, right) (WikiPathways RDF), Anwesha Dutta (bottom, 2nd from the left) (flux visualization), Stefan van Helden (not on the picture) for the RI PathVisio plugin