Systems Immunology

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Over the past decade, experimental and computational approaches for deep molecular and cellular profiling have become readily available and widely used for profiling the immune system both in human and in model organisms. These techniques include genomic, epigenomic, transcriptomic, proteomic, metabolomic, antibody-omic and cellular phenotypic datasets. The broad focus of this systems immunology collection is on technologies that can be used to generate and interrogate combinations of these datasets in a principled fashion to uncover phenotypes and mechanisms underlying immunological states and disorders, and predictive dynamical models that can connect such high-throughput data to phenotypic/cell-state aspects. We are interested in manuscripts describing both computational and high-throughput experimental techniques including novel devices that can be used to generate and/or analyze these multi-omic datasets. A major emphasis of the collection will also be advances in single-cell technologies (e.g., scRNA-seq, scATAC-seq) either on the computational and/or experimental fronts. Manuscripts do not necessarily need to include both aspects; studies describing either novel computational approaches for the analyses of multi-omic datasets or creative experimental techniques for generating one or more of these datasets are welcome. However, manuscripts simply describing computational methods without demonstrating their applications on real-world datasets will not be considered a good fit for this collection.

Graphical representation of the layered approach to the model. Cytokine concentrations are inputted into the model to determine a portion of CD4+ T cell differentiation.


  • Mohit Kumar Jolly

    Indian Institute of Science Bangalore

  • Jishnu Das

    University of Pittsburgh School of Medicine

Dr. Jolly leads the Cancer Systems Biology group at Indian Institute of Science, Bangalore. His research focus is on decoding the underlying design principles of various cell-fate decision networks as well elucidating the emergent dynamics of phenotypic plasticity and non-genetic heterogeneity in multiple biological contexts – cellular differentiation and reprogramming, cancer metastasis and resistance against many therapies.



Dr. Das is an Assistant Professor leading a computational systems immunology research group at the Center for Systems Immunology, University of Pittsburgh. His research focuses on the development and use of novel network systems and functional genomic approaches to perform multi-scale integration of genomic and epigenomic datasets with biological networks to identify molecular phenotypes underlying a range of immunological disorders. He also uses high-dimensional statistical and interpretable machine learning techniques to integrate multi-omic datasets and elucidate molecular mechanisms of immune regulation and dysregulation.