IN – Track B



Biological Validation of Mathematically Predicted Algebraic Conditional Expectation Structures Arising in Gene Regulatory Networks

Project Heads

Vikram Sunkara, Mir-Farzin Mashreghi, Gitta Anne Heinz, Heike Siebert, Tim Conrad

Project Members

N. Alexia Raharinirina (ZIB)

Project Duration

01.03.2020 – 31.12.2020

Located at



In this project we will use our recently established theory–algebraic conditional expectation structures in Gene Regulatory Networks to design a new causal graph model. We will apply this model to the problem of T-cell over-activation in autoimmune disease, which will be the first step towards oligonucleotide therapies for chronic inflammatory diseases.

Project Webpages

Selected Publications

V. Sunkara, G. A. Heinz, ..., M.F. Mashreghi, A. Lang
Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint.
(To Be Announced)

M. Babic, ..., M.F. Mashreghi, ..., Chiara Romagnani.
NK Cell Receptor NKG2D Enforces Proinflammatory Features and Pathogenicity of Th1 and Th17 Cells
J Exp Med. 2020 Aug 3;217(8):e20190133. doi: 10.1084/jem.20190133.

C. Tizian, ... , M.F. Mashreghi  , A. Diefenbach, C. Neumann
c-Maf Restrains T-bet-driven Programming of CCR6-negative Group 3 Innate Lymphoid Cells
Elife. 2020 Feb 10;9:e52549. doi: 10.7554/eLife.52549.

Selected Pictures

Single cell RNA-seq(scRNAseq) poses a novel data challenge. Unlike problems in physics and engineering, biological samples are heterogeneous and cannot be observed repeatedly. The process of extracting the transcription of a cell involves destroying it, hence, we can only capture independent snapshots of the transcription. Cells do not adhere to the experimental wall clock, each cell has its own internal clock. Hence, in practice, the cells are sequenced together, then the states of the cells are determined through clustering and then the transitions between the cells are inferred through manifold learning. To date, inferring the gene regulatory networks of cells using scRNA-seq data is an ongoing challenge.

Recently, it was found that expression of circular RNAs in naïve T-cells could regulate the T-cell over-activation in autoimmune diseases like Lupus [1]. Specifically, it was seen experimentally that by changing the concentration of the circular RNA circIkzf1, the T-cell activation threshold could be modulated. However, the causal pathway of circIkzf1 affecting the T-cell activation is still illusive. In this project we aim to find the causal signalling pathways through which circIkzf1 modulates T-cell activation in autoimmune diseases (like Lupus and Rheumatism).

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