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Computational biology

To interpret the complex data sets generated an innovative computational biology strategy is required. The faculty of the Center have experience with both bioinformatics and systems immunology, with substantial expertise in integrating large-scale genomic data (e.g., DNA variation, transcriptional profiling, epigenetics) with immunological and clinical phenotype data to gain a deeper understanding of pathophysiology of immune-mediated diseases.

(a) Bioinformatics. A robust computational framework is required to store, process and analyze the enormous amounts of data generated by these experiments. In addition, other data sets have been generated that provide a molecular understanding of how proteins interact, epigenetic re-programming occurs, etc. We have access to a state-of-the-art computer cluster and a series of analytical platforms to integrate data across all experiments described in previous modules, as well as data generated by others.

(b) Systems immunology. Integration of detailed genomic data sets serves a central role in unraveling the pathogenic mechanisms that lead to immune-mediated disease.  We utilize methods of pathway and network reconstruction to unravel the molecular abnormalities of human immune cells. We use profiles generated from immunophenotyping and genome-wide analysis to identify key molecular pathways in each cell type or mixtures of interacting cell types and use time dependent and stimulus dependent approaches to expand network development around selected pathways and stimuli. In this way, we can refine hypotheses about mechanisms underlying specific immune-mediated diseases, and test these hypotheses directly in samples derived from patients.

(c) Epidemiology and Statistics. Advanced methodologies are needed to study biomarker and genetic associations with clinical phenotypes. Our team has expertise in statistical methods and analyses through the Biometry Core of the Multidisciplinary Clinical Research Center. Our faculty are experts in study design of longitudinal cohorts and outcomes studies, pharmacoepidemiology, and pharmacogenetics and the use of the EMR for outcomes research. We work closely with the bioinformatics and systems immunology groups to integrate statistical analyses across data sets.

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