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Dr. Peters and his team are developing tools to analyze and predict what parts of a pathogen or allergen are targeted by immune responses. Several of the molecular mechanisms involved in these processes have been well characterized experimentally. By analyzing patterns in the experimental data, it is possible to create predictive computational models. These models can be applied to scan allergens or pathogens in silico for likely immune response targets. Identifying these targets aids in the rational development of treatments and diagnostics. The resulting computational tools are made freely available as part of the Immune Epitope Database Analysis Resource (http://tools.immuneepitope.org) The Immune Epitope Database itself catalogs and organizes immune epitope data, which requires transforming free text information from journal publications into a structured format. To make optimal use of the stored information, it is desirable to connect it with information stored elsewhere. For example, one could ask what the variability of an immune response target in different strains of a pathogen is. This requires connecting the IEDB data to other resources storing genomic information. Doing this efficiently requires a community consensus on knowledge representation standards. Dr. Peters team is contributing to such consensus building and standardization efforts through active work on scientific community initiatives: The Ontology of Biomedical Investigations (OBI, http://obi-ontology.org/), and the NIAID data interoperability working group.

News & Events

11/16/2009 - NBC San Diego
Past Flu Infection May Boost H1N1 Immunity
.mov | mp4 | podcast

11/16/2009 - News Release & Media Coverage
La Jolla Institute Finds Previous Seasonal Flu Infections May Provide Some Level of H1N1 Immunity