Dennis Goldfarb, PhD

Dennis Goldfarb, PhD

Assistant Professor of Cell Biology & Physiology

The Goldfarb Lab is focused on computational mass spectrometry and proteomics with the goal of achieving comprehensive protein identification and quantification in complex biological samples.

Research Interests

Dennis Goldfarb, PhD’s research focuses on computational mass spectrometry, proteomics, and their applications in biology. His lab develops open-source solutions for novel data acquisition strategies, data analysis, and visualization tailored to the unique challenges of each experiment. Dr. Goldfarb aims to increase the throughput and reproducibility of large-scale projects with automated pipelines that leverage high-performance computing. Recently his research has concentrated on instrumentation, protein complex identification, and de novo peptide sequencing.

Dr. Goldfarb received his PhD in Computer Science from The University of North Carolina at Chapel Hill. During this time he studied full-time in a cancer cell biology laboratory and became proficient in the operation of liquid chromatography and mass spectrometry instrumentation, experimental design, and subsequent data analysis. He developed methods to compute isotope distributions of fragment ions, deconvolve chimeric mass spectra, and to predict protein-protein interactions from affinity purification – mass spectrometry experiments. Through his many collaborations, Dr. Goldfarb has and continues to contribute to our understanding of the human microbiome, histone code, drug-target discovery, and cancer.

Professional Education
  • BS: Rennselaer Polytechnic Institute, 2010, Computer Science
  • PhD: University of North Carolina-Chapel Hill, 2018, Computer Science
  • Postdoc: University of North-Chapel Hill, 2018-2019, Mass Spectrometry and Proteomics
Graduate & Fellowship Program Affiliations

Goldfarb Lab

McDonnell Sciences Building (MS: 8228-0012-04)

Mass Spectrometry | Proteomics | Bioinformatics

We develop software, algorithms, and workflows for mass spectrometry and proteomics experiments. Using machine learning, statistical modeling, and computer science techniques we optimize a mass spectrometer’s data acquisition strategy in real-time to improve identification, quantification, and create novel analytical capabilities. We are focused on protein-protein interactions, protein complexes, and de novo peptide sequencing.