Galit Lahav

Galit Lahav serves as the Novartis Professor and Chair of the Department of Systems Biology at Harvard Medical School, Harvard University. Her laboratory pioneers quantitative single-cell analysis to decipher cellular decision-making processes through signal transduction dynamics, with primary focus on the p53 tumor suppressor pathway central to cancer biology.

Her educational background includes:

• PhD in Biology from the Technion, Israel Institute of Technology (2001)
• Postdoctoral fellowship at the Weizmann Institute of Science, Israel (2003)
• Research at Harvard’s Bauer Center for Genomics Research (2003-2004)

Dr. Lahav employs live single-cell imaging with fluorescent reporters to track protein dynamics (notably p53) at high temporal resolution, revealing how cell-to-cell variability in dynamical behaviors correlates with cellular fates like growth, death, or differentiation. Her work addresses fundamental questions about heterogeneous drug responses in cancer and normal tissues, leveraging systems biology approaches to decode signaling network logic.

Her research demonstrates that p53 exhibits pulsatile dynamics in individual cells following DNA damage, with pulse number determining cell fate decisions. This quantitative framework extends to DNA repair and cell growth networks, providing mechanistic insights into cancer heterogeneity where over 50% of human cancers feature p53 pathway inactivation.

Dr. Lahav's scientific recognition includes:

• Vilcek Prize for Creative Promise in Biomedical Science
• Excellence in Mentoring award

She actively cultivates leadership through science communication workshops and mentorship programs for junior faculty and postdocs. Currently spearheading Harvard's initiative to enhance recruitment and support systems for women faculty, she aims to establish the institution as a premier destination for women in science through structural policy reforms.

The Lahav Lab maintains a quantitative microscopy core for high-resolution single-cell analysis, investigating how dynamical network properties govern cellular decision-making. Their work bridges theoretical modeling with experimental validation to develop predictive frameworks for therapeutic interventions targeting cancer cell fate specification.