Researchers from the life sciences research institute in Flanders (VIB) and Ghent University (UGent) have used analysis algorithms from social networks to study intercellular communication.
In order to study communication between cells, researchers from the VIB-UGent Center for Inflammation Research (IRC) have developed a method called NicheNet. Using this technique has helped them to gain insight into how the gene expression of cells is regulated by interacting cells. NicheNet has a broad range of potential applications in the field of immunology but also in the study of tumour biology.
In the case of multicellular organisms, cells don’t function on their own, but produce a signal to molecules that influence gene expression in how the cells interact. This intercellular communication plays an important role in many biological processes, such as the development and functioning of cells.
To deal with this issue, they used machine learning (a form of artificial intelligence) and statistical techniques. These included network algorithms, also used to analyse social networks.
The study of intercellular communication is not only important for understanding fundamental biology, but also for gaining insights into diseases such as cancer. Interactions between cancer cells and other cells in the microenvironment of a tumour are crucial for its growth.