Hratch M. Baghdassarian

I am a Cancer Research Institute Immuno-Informatics Postdoctoral Fellow in the Lauffenburger  Lab, using neural network modeling approaches to decipher immune cell signaling pathway activity at single-cell resolution. I recently received my PhD in Bioinformatics and Systems Biology in the Lewis Lab at the University of California, San Diego.  My full CV can be found here.

Research Interests

Multicellular Systems Immunology

I develop computational and mechanistic models to understand how collections of immune and tissue-resident cells coordinate to produce tissue-scale function, with a particular focus on inflammation.  Inflammation is a prototypical multicellular process where coordinated responses can succeed or fail, leading to resolution or chronic disease. Its complexity across scales (molecular → cellular → tissue) makes it a compelling testbed for systems models that aim to predict and explain emergent behavior.

Leveraging single-cell omics and genome-scale modeling, my work seeks to explain how intracellular states, cell–cell communication, and shared resource constraints give rise to higher-order behaviors—such as division of labor, robustness, and failure modes that drive disease. Across these areas, my goal is to synthesize mechanistic and statistical modeling approaches to generate testable hypotheses and guide therapeutic strategies.To achieve this, I integrate a wide-range of mathematical approaches, including linear programming, tensor decomposition, machine learning, and neural networks. My research spans three tightly integrated areas:

1. Cell–cell communication and multicellular coordination: I develop methods to infer and analyze cell–cell communication networks at single-cell resolution. These approaches enable the identification of key ligand-receptor mediators and cell circuits mediating homeostasis. The iterative feedback between extracellular cues and intracellular state leads coordinates collective cell behavior, and I model and integrate the various biological systems involved in this loop -- protein and metabolite ligands as extracellular communicators, metabolism and gene expression as actuators of intracellular state, and signaling and secretion serving as the link between the two. 

Key Papers: PMID: 35760817, PMID: 38238518, PMID: 41278889, PMID: 34171228

1. Context-aware single-cell analysis. In addition to building standard analysis pipelines, I design my tools specifically to generalize across multiple contexts (e.g., time, perturbation, disease state) and to leverage the subpopulation heterogeneity available in single-cell resolution measurements, enabling mechanistic interpretation of how cellular programs adapt across inflammatory conditions. Most recently, I am developing mechanistic methods that predict single-cell perturbation responses.  

Key Papers: PMID: 35760817, PMID: 37256972, PMID: 40215972

1. Uncovering resource allocation principles and cell objectives in human tissues.  A resource allocation framework--the optimization of cellular objectives under resource constraints--can provide a unique perspective and insights into intracellular pathway activity, multicellular coordination, and the genotype-phenotype relationship. I develop genome-scale models that couple gene expression, metabolism, and secretion to quantify how cells allocate limited resources to achieve functional objectives. By extending these models beyond growth-centric assumptions, I study how immune cells optimize diverse tasks—such as proliferation, cytokine secretion, migration, and stress responses—and how these objectives shape functional heterogeneity in inflammatory tissues.

Key Papers: PMID: 38215956, PMID: 41380680

Publications

First or Corresponding Author

1. O. Nordenstorm*, H. Baghdassarian*, D.A. Lauffenburger, A. Nilsson. “Biologically informed neural network models are robust to spurious interactions via self-pruning.” In revision, Bioinformatics. https://doi.org/10.1101/2025.10.24.684155

2. R. Sharma*, N. Meimetis*, A. Begzati, SD. Nagar, B. Kellman, H. Baghdassarian. "Joint linear modeling of transcriptomics and proteomics is predictive of cancer metastasis." Under review, NPJ Systems Biology & Applications. https://doi.org/10.1101/2025.02.15.638428.

3. H. Baghdassarian*, D. Dimitrov*, E. Armingol*, J. Saez-Rodriguez, N. Lewis.  "Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples." Cell Reports Methods (April 2024). https://doi.org/10.1016/j.crmeth.2024.100758. 

4. H. Baghdassarian, N. Lewis. "Resource Allocation In Mammalian Systems". Biotechnology Advances. (Jan. 2024). https://doi.org/10.1016/j.biotechadv.2023.108305.

5. H. Baghdassarian*, S. Blackstone*, O. Clay, et al. "Variant STAT4 and Response to Ruxolitinib in an Autoinflammatory Syndrome." NEJM. (May 2023). https://doi.org/10.1056/NEJMoa2202318.

6. E. Armingol*,  H. Baghdassarian*, C. Martino, A. Perez-Lopez, C. Aamodt, R. Knight, N. Lewis. "Context-aware deconvolution of cell-cell communication with Tensor-cell2cell".  Nature Communications (June 2022). https://doi.org/10.1038/s41467-022-31369-2.

7. A. Chiang*, H. Baghdassarian*, B. Kellman, B. Bao, J. Sorrentino, C. Liang, C. Kuo, H. Masson, N. Lewis. "Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy". Journal of Biomedical Science (June 2021). https://doi.org/10.f1186/s12929-021-00746-2.

8. B. Kellman*, H. Baghdassarian*, T. Pramparo, I. Shamie, V. Gazestani, A. Begzati, S. Li, S. Nalabolu, S. Murray, L. Lopez, K. Pierce, E. Courchesne, N. Lewis. “Multiple Freeze-Thaw Cycles Lead to a Loss of Consistency in poly(A)-Enriched RNA Sequencing.” BMC Genomics (Jan. 2021). https://doi.org/10.1186/s12864-021-07381-z.

Co-Author

9. A. Datta, K. Bridges, H. Baghdassarian, L. Bahlmann, D. Gong, E. Tevonian, B. Joughin, D. Lauffenburger. "Single-cell RNA-sequencing reveals tumor microenvironment composition and prior therapy modulate response to Axl inhibition." In Submission, NPJ Systems Biology & Applications. 

10. E. Armingol, R. Larsen, M. Cequeira, H. Baghdassarian, N. Lewis. “Tensor-cell2cell v2 unravels the coordinated dynamics of protein- and metabolite-mediated cell-cell communication.” Accepted, Bioinformatics. https://doi.org/10.1101/2022.11.02.514917.

11. H. Masson*, J. Tat*, P. Di Giusto*, A. Antonakoudis, I. Shamie, H. Baghdassarian, M. Samoudi, C. Robinson, C. Kuo, N. Koga, S. Singh, A. Gezalyan, Z. Li, A. Movsessian, A. Richelle, N. Lewis. “A reconstruction of the mammalian secretory pathway identifies mechanisms regulating antibody production”. Cell Systems. (Dec. 2025). https://doi.org/10.1016/j.cels.2025.101453.

12. M. Cardone, H. Baghdassarian, M. Khalaj, K. Sivakumar, S. Hwang, S. Gebreyohannes, K. Takeda, Y. Jang, N. Lewis, M. Norcross, M. Puig. “Insights into regulatory T-cell and type-I interferon roles in determining abacavir-induced hypersensitivity or immune tolerance”. Frontiers in Immunology (June 2025). https://doi.org/10.3389/fimmu.2025.1612451.

13. C. Perez, A. Garmilla, A. Nillson, H. Baghdassarian, K. Gordon, L. Lima, B. Smith, M. Maus, D. Lauffenburger, M. Birnbaum. “Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence.” Cell Systems. (April 2025). https://doi.org/10.1016/j.cels.2025.101260.

14. E. Armingol, H. Baghdassarian, N. Lewis. “Next-generation tools for studying cell–cell interactions and communication.” Nat. Rev. Genet. (Jan. 2024). https://doi.org/10.1038/s41576-023-00685-8.

15. E. Armingol, A. Ghaddar, C. Joshi, H. Baghdassarian, I. Shamie, J. Chan, H. Her, S. Berhanu, A. Dar, F. Rodriguez-Armstrong, O. Yang, E. O’Rourke, N. Lewis. “Inferring a spatial code of cell-cell interactions and communication across a whole animal body.” PLOS Comp. Bio. (Nov. 2022) . https://doi.org/10.1371/journal.pcbi.1010715.

16. C. Kuo,  A. Chiang, H. Baghdassarian, N. Lewis. "Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation". Cell Systems. (June 2021). https://doi.org/10.1016/j.cels.2021.06.001.

17. H. Pinkard, H. Baghdassarian, A. Mujal, E. Roberts, K. Hu, D. Friedman, I. Malenica, T. Shagan, A. Fries, K. Corbin, M. Krummel*, L. Waller*. “Learned adaptive multiphoton illumination microscopy for large-scale immune response imaging”.  Nat Commun. (March 2021). https://doi.org/10.1038/s41467-021-22246-5.

18. S. Palluk*, D. Arlow*, T. Rond, R. Bector, J. Kang, H. Baghdassarian, A. Truong, P. Kim, A.Singh, N.Hillson, J.Keasling. "De novo DNA synthesis using polymerase-tethered nucleotides." Nat. Biotechnol. (June 2018). https://doi.org/10.1038/nbt.4173.

*contributed equally to work

News and Media

• STAT4 Variants

• Behind the Paper: Tensor-cell2cell: Unraveling the complex cell-cell communication patterns driving phenotype

• LIANA and Tensor-cell2cell unified framework

• CRI Fellowship: Profile

Contact and Links:

• Email: hmbaghda at mit dot edu