Matteo Pellegrini

Matteo Pellegrini is a Professor at UCLA with appointments across multiple departments: Human Genetics, Medicine, Molecular, Cell and Developmental Biology, and the California NanoSystems Institute (CNSI). He serves as Director of both the QCB Collaboratory, which runs data analysis workshops and consultations, and the Computational Biology Interdepartmental Program.

Dr. Pellegrini's research focuses on computational biology and biophysics, with particular emphasis on developing novel computational approaches to reverse engineer biomolecular networks and interpret genomic data. His work spans epigenetics, DNA methylation analysis, aging research, and development of algorithms for biological data analysis. His laboratory has made significant contributions to epigenetic clock development, methylation pattern analysis across species, and computational methods for genomic interpretation.

Analysis of Dr. Pellegrini's recent publications reveals a strong focus on computational methods for analyzing DNA methylation patterns across diverse biological contexts. His work spans model organisms from Xenopus to canids, and addresses clinical applications in cancer, transplantation medicine, aging, and infectious disease. A recurring theme is the development of sophisticated algorithms like MicroBayesAge and BayesAge 2.0 that translate complex methylation patterns into biologically meaningful insights about aging, disease risk, and physiological states.

Dr. Pellegrini has secured significant NIH funding as Principal Investigator for multiple projects including the Biomedical Big Data Training Grant (T32LM012424, T32CA201160), Enhancing the International Molecular Exchange (R01GM123126), and DNA methylation and its relationship to gene expression and physiologic traits (R01GM095656). He also serves as Co-Principal Investigator on several other NIH-funded projects related to metabolic reprogramming in SARS-CoV-2, dental genomics training, and heart scar remodeling.

His laboratory operates at the intersection of computational biology and experimental validation, with research spanning algorithm development, epigenetic clock construction, DNA methylation analysis across species, and applications to clinical problems in transplantation, cancer, and aging. The QCB Collaboratory he directs provides computational support and training to the broader UCLA research community.