Publications

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.