Sahand Hormoz

Sahand Hormoz

Assistant Professor
Department of Systems Biology, Harvard Medical School/ Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
Associate Member, Broad Institute of MIT and Harvard/ Affiliate Faculty, Harvard Stem Cell Institute
Sahand Hormoz
We use synthetic biology, single-molecule imaging, and single-cell analysis combined with mathematical modeling to understand the dynamics of cell state transitions in development and disease.
During development, for tissue maintenance, and in diseases, as cells proliferate, they transition between functionally and molecularly distinct states. Dysregulation of transitions between cell states can lead to pathologies such as cancer. In many biological systems, we would like to know which transitions can occur, in what sequence, and at what rates. Current single-cell methods have expanded our ability to identify distinct cell states but not our ability to directly measure their dynamics. For example, state of the art single-cell techniques can measure the expression levels of thousands of genes, but they destroy the cells in the process, and provide only static snapshots.
 
In our lab, we combine experiments and theory to understand the complex dynamics of cell state transitions in development and in disease. To do so, we expand and combine emerging experimental techniques. For example, we use time-lapse microscopy to track the lineage history of individual cells as they divide, followed by single-molecule imaging to readout the expression levels of multiple genes in the same cells. We also use synthetic biology to engineer novel genetic circuits that can retain the history of major transitions in each cell. One of our favorite model systems is mouse embryonic stem (ES) cells in culture, which can rehash slices of development on a dish, providing us with a rich playground.
 
At the same time, we also develop new theoretical frameworks to decipher the data generated in our experiments: for example, methods for inferring dynamics from single-cell lineage trees with endpoint gene expression measurements. Often inspired by concepts in statistical physics, we have used ideas from geometry, critical phenomena, and at times number theory. Ultimately, we seek to find general theoretical principles to understand the stochastic processes and collective dynamics that emerge in populations of proliferating cells.
 
Selected Publications:
 
KL Frieda*, JM Linton*, S Hormoz*, J Choi, KK Chow, ZS Singer, MW Budde, MB Elowitz, and L Cai.  Synthetic recording and in situ readout of lineage information in single cells.  Nature 541 (7635): 107-111 (2017). * equal contribution
 
S Hormoz*, ZS Singer*, JM Linton, YE Antebi, BI Shraiman,  MB Elowitz.  Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements.  Cell Systems 3 (5): 419-433 (2016). Featured on the cover. * equal contribution.
 
S Hormoz, N Desprat, BI Shraiman.  Inferring epigenetic dynamics from kin correlations.  PNAS 112, (18) E2281-E2289 (2015).
 
S Hormoz.  Cross talk and interference enhance information capacity of a signaling pathway. Biophysical Journal 104 (5), 1170-1180 (2013). Featured on the cover.

Contact Information

360 Longwood Avenue
Longwood Center Building,
9th Floor, Room 310 (LC-9310)
Boston, MA 02115
p: 617 582-7361