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The Brubaker Lab takes an integrated computational and experimental systems biology approach to understand mechanisms of host-microbiome interactions and immune cell biology, primarily, but not exclusively, in conditions with implications for female reproductive health. 

Areas of particular interest include infertility, progesterone and estrogen signaling, endometriosis, and the role of the gut and vaginal microbiomes in these conditions. We also have active projects and collaborations in inflammatory bowel diseases, neurodegenerative disorders, musculoskeletal interactions with the gut microbiome, and sepsis. 

The lab is organized into three research areas:

1. Systems Immunology: The immune system is a complex, adaptive network of cells and tissues that dynamically respond to injury, inflammation, and infection. To develop predictive models of immune behavior in response to infectious insult or therapeutic intervention, we employ integrative computational approaches to bridge single cell biology with higher order phenotype to develop mechanistic hypotheses and characterize complex disease. 
2. Microbiome Pharmacology: While microbiome diversity and individual taxa can be linked to specific host phenotypes and pathway disruptions, a detailed understanding of how microbiomes and their products may act as or interfere with therapeutic interventions is lacking. Our group seeks to develop a taxonomy of therapeutic actions of all constituents of the human microbiome through integrative models of microbiome multi-omics data. 
3. Preclinical Translation Modeling: While animal and in vitro experimental models are essential tools for testing hypotheses derived from computational analysis of complex human data (reverse-translation), the results of those studies must be related back to the human physiological context (forward-translation). To bridge this gap, we develop computational methods to identify biological signals in experimental systems predictive of phenotype and therapeutic outcome in humans. 

In our view, Systems Biology encompasses an approach that embraces the complexity of biological systems. The actions of individual pathways or signaling networks in disease must be understood in the context of the broader cellular communication network, tissue, and organism. Computational approaches are fundamental to understanding this complexity, but must either be hypothesis driven themselves, or endeavor to yield experimentally testable hypotheses.