Research Areas

Reproductive and developmental biology is anchored by faculty at the Magee-Womens Research Institute whose work spans the full arc of reproduction. Topics include germline formation, early embryogenesis, and the developmental programming of offspring health. Faculty dissect meiotic recombination and DNA repair in germ cells, errors in which drive miscarriage and infertility, reproductive aging and the ovarian reserve, spermatogonial stem cell transplantation and fertility preservation, and epigenetic reprogramming and genomic imprinting in early embryos. Others study how the maternal microbiome shapes offspring brain and metabolic development and how engineered genetic circuits can direct stem cells to self-organize into vascularized organoids. Much of this work is intrinsically quantitative and draws on single-cell genomics and computational modeling, and advanced imaging analysis.

CMSB faculty research centers on organ regeneration across many systems including liver, kidney, heart, and pancreas. Model systems to dissect the molecular mechanism of tissue and organ regeneration include: zebrafish and flies, powerful in vivo models of innate regenerative capacity; mice, a mammalian model for human regeneration. Faculty also engineer regeneration from the bottom up, using synthetic biology and genetic circuits to direct iPSCs into vascularized organoids, combining quantitative imaging with theory to control the self-organization of brain, liver, kidney and cardiac organoids. Other faculty study stem cells in disease and repair, including germline stem cells, carcinoma-associated mesenchymal stem cells in the ovarian tumor microenvironment, tumor-virus effects on cancer cell stemness, and wound healing and tissue repair. As across the program, much of this work pairs experimental models with single-cell mutli-omics and computational analysis. 

CMSB's dual-fluency thesis is embodied by faculty who develop patient-specific tumor models and the computational methods to interpret them hand-in-hand. The experimental engine is increasingly patient-derived cancer organoids, which recapitulate a tumor's genotype, heterogeneity, and drug responsiveness far better than cell lines, enabling functional precision oncology. These models are converted into high-dimensional data through single-cell and spatial multi-omics, combined with quantitative imaging, and bioengineered microfluidic tumor-microenvironment platforms. Deep-learning models that predict drug sensitivity and genetic dependencies coupled with Large Language Models that integrate multi-omic data and large-scale clinical and genomic datasets.

This is the program's largest and most foundational research focus. Faculty are interested in understanding how cells are built and operate at the molecular level and how those mechanisms scale up to the physiology of tissues and organs. Its core themes include membrane and organelle dynamics involved in trafficking, lipid and receptor signaling, which connect to deep institutional strengths in epithelial and renal-electrolyte physiology (ion channels and transporters governing salt, water, and blood-pressure balance) and in cardiovascular and vascular cell biology. Cutting across these systems are mechanistic threads in the cytoskeleton, cell adhesion, and tissue mechanics; in metabolism, mitochondria, redox signaling, and aging; and in genome maintenance and gene expression, with further depth in the cell biology of sensory systems, reproduction, and host–pathogen interactions. The toolkit is mechanistic and increasingly quantitative with advanced microscopy, structural biology, model organisms, organ-on-chip systems, and spatial multi-omics.

CMSB includes a strong cohort of computational biologists whose core contribution is building new algorithms, tools, and theoretical frameworks. Their methods span structure-based drug design and molecular docking, deep mutational scanning and variant-effect prediction for clinical genome interpretation, and machine-learning frameworks that derive the governing equations of single-cell dynamics and cell-fate transitions. In addition, they develop tools for interpretable machine learning and high-dimensional immune and multi-omic data, as well as models of single-cell signaling dynamics. While these labs lead with algorithm development, many also run their own wet bench experiments to generate the data their methods exploit or may do so by co-mentoring PhD students.

CMSB has a vibrant research community anchored institutionally by the University of Pittsburgh Aging Institute that investigates why we age and how the rate of biological aging might be slowed. Its labs span the mitochondrial, metabolic, oxidative-stress, and autophagy pathways of mammalian aging, how endogenous DNA damage and genome instability drive cellular senescence, with efforts to develop molecular indices of biological age, the conserved transcription factors linking reproduction, metabolism, and longevity in C. elegans, the metabolic reprogramming of aging and neurodegeneration modeled in Drosophila and mice, and single-cell genomics of brain aging.

The principle that biological function emerges from networks of interacting components is best understood by integrating quantitative measurement, computation, and modeling rather than studying parts in isolation. Faculty with a systems biology focus are in systems immunology, where gene-regulatory networks and transcription-factor circuits controlling immune-cell fate are assembled from functional genomics and computational modeling. Complementary strengths span systems and network physiology from pulmonary vascular disease to the systems genetics of congenital heart disease. Other areas include synthetic, engineered biological systems built from gene circuits and reconstituted microenvironments.