Towards a Cytokine-Cell Interaction Knowledgebase of the Adaptive Immune System

Shai S. Shen-Orr1,2, Ofir Goldberger2, Yael Garten3, Yael Rosenberg-Hasson4, Patricia A. Lovelace4,5, David L. Hirschberg4, Russ B. Altman6, Mark M. Davis2,7, and Atul J. Butte1,8

1 Stanford Biomedical Informatics Research, Department of Medicine, 2Department of Microbiology & Immunology, 3Stanford Biomedical Informatics Training Program, 4Stanford Human Immune Monitoring Center, 5Stem Cell Biology and Regenerative Medicine, 6Departments of Bioengineering and Genetics, 7The Howard Hughes Medical Institute, 8Department of Pediatrics, Stanford University, Stanford, CA 94305-5479, USA

Pacific Symposium on Biocomputing 14:439-450(2009)


The immune system of higher organisms is, by any standard, complex. To date, using reductionist techniques, immunologists have elucidated many of the basic principles of how the immune system functions, yet our understanding is still far from complete. In an era of high throughput measurements, it is already clear that the scientific knowledge we have accumulated has itself grown larger than our ability to cope with it, and thus it is increasingly important to develop bioinformatics tools with which to navigate the complexity of the information that is available to us. Here, we describe ImmuneXpresso, an information extraction system, tailored for parsing the primary literature of immunology and relating it to experimental data. The immune system is very much dependent on the interactions of various white blood cells with each other, either in synaptic contacts, at a distance using cytokines or chemokines, or both. Therefore, as a first approximation, we used ImmuneXpresso to create a literature derived network of interactions between cells and cytokines. Integration of cell-specific gene expression data facilitates cross-validation of cytokine mediated cell-cell interactions and suggests novel interactions. We evaluate the performance of our automatically generated multi-scale model against existing manually curated data, and show how this system can be used to guide experimentalists in interpreting multi-scale, experimental data. Our methodology is scalable and can be generalized to other systems.

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