The communication between cells plays a crucial role in disease development and response to treatment. These interactions are governed by sender and receiver proteins called ligands and receptors. The resulting complex system of cell-cell communications can be viewed as a network where the nodes are cells and (directed) links represent ligand-receptor interactions.
The ability to characterize cell-cell interaction networks for individual patients would be extremely valuable to understand how intercellular communication affects disease progression and response to treatment. While it is possible to determine for a given patient which cell types, ligands and receptors are present. Reconstructing the patient-specific inter-cellular network from this type of data provides a great challenge. If overcome, this could be a powerful tool to support patient stratification for optimal treatment.
In this project we aim to develop a theoretical and computational framework for inferring intercellular communication networks based on transcriptomics data (cell types, the expression of ligands, and receptors) in individual patients, using approaches from probability theory, statistics, and network science.
The project is part of the Immuno-Engineering project of the Institute for Complex Molecular Systems and is conducted in collaboration with dr. Federica Eduati from the department of biomedical engineering and PhD student Mike van Santvoort.
Relevant papers: