My central goal is to build predictive, mathematical models of the molecular network interactions that underlie human physiology and disease. These models are informed through integration of publicly available datasets with systems-level molecular measurements from carefully designed perturbation time-course experiments. In my current postdoctoral research position, as a member of both Rich Bonneau’s lab at the Simons Center for Data Analysis and New York University and Dan Littman’s lab at the NYU School of Medicine, my focus is mathematical modeling of the gut ecosystem and gut mucosal immunity, especially in the context of autoimmunity. Specifically, I am interested in (1) how the many immune-cell populations associated with the gut regulate and are influenced by the gut-resident microbial populations (and vice versa), (2) how dysregulation of this ecosystem contributes to autoimmune disease, and (3) what molecular-level perturbations can be designed to alter the course of autoimmune disease.
In collaboration with members of the Littman lab, I design perturbation time-course experiments, in which mice are exposed to (1) immunological perturbations involving immune-cell types and gene pathways involved in human autoimmune disease and (2) microbial perturbations (e.g., introduction of a microbial species) known to affect host immunity and murine models of autoimmune disease. To model the system at an ecological level, we will measure the population-level responses of immune lineages and microbiota (using FACS and 16S sequencing techniques) to these perturbations and learn the ecological interactions using our recently published sparse Gaussian graphical modeling framework for ecological association inference (SPIEC-EASI). At a molecular level, we have focused on characterizing gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) response in key immune-cell types to these perturbations in vivo. To this end, I am adapting the Bonneau lab’s Inferelator algorithm for transcriptional regulatory network inference; this involves developing frameworks to (1) infer transcription-factor activities from the ATAC-seq data (to be used as predictors of gene expression) and (2) leverage and integrate relevant existing in vitro datasets into the inference procedure.
My Google Scholar profile can be found here.
Ph.D., Computational and Systems Biology (2012)
Massachusetts Institute of Technology, Cambridge, MA
B.A., Biochemistry, with Minor in Mathematics (2006)
Oberlin College, Oberlin, OH
Kurtz ZD, Mueller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226.
Miraldi ER, Sharfi H, Friedline RH, et al. Molecular network analysis of protein-tyrosine phosphorylation and altered lipid metabolism in liver-specific PTP1b deletion mice. Integr Biol. 2013;5(7):940-963. PMID: 23685806.
Huang PH, Miraldi ER, Xu AM, et al. Phosphotyrosine signaling analysis of site-specific mutations on EGFRvIII identifies determinants governing glioblastoma cell growth. Mol Biosyst. 2010;6(7):1227-37. PMID: 20461251.
Miraldi ER, Thomas PJ, Romberg L. Allosteric models for cooperative polymerization of linear polymers. Biophys J. 2008;95(5):2470-2486. PMID: 18502809.