Project

Food for the Gatekeeper

The homeostasis of the small intestine and its mucosal immune system can be modulated by various food-products. In order to efficiently study, but also predict, the effects of food-products, we propose the use of a three-dimensional approach including a novel system biology-based mathematical model system.

By combining current knowledge acquired by the partners, available literature, and combined in regulatory networks by publicly available software tools, we will construct an in silico pathway-network model of the most important hubs for local and systemic immune signals of the gut and mucosal associated immune cells. This model framework, which allows us to formulate testable hypotheses on the effect of bioactive food-products in general but here with an emphasis on polysaccharides, and pre- and probiotics, will be put to the test by comparing in silico models to in vitro results.

Available in vitro data have been derived from several projects which made use of intestinal epithelial cells (Caco-2) and innate immune cells (THP-1). These data will be expanded by on-going projects using similar cell lines, co-cultures of Caco-2 with immune cells like dendritic, T and B cell responses as well as in vivo data from mice and humans exposed to the same food compounds. Besides transcriptomics and cytokine data used for analysis of cell responses, new generated data will contain intracellular signals by making use of reporter systems which can monitor translocations of transcription factors in the cell. The models and data analysis should therefore implement multilevel responses.

An iterative interaction between the dry modellers and the scientists that perform wet experiments in complementary projects should lead to development of models that assist on experimental design and that can support the understanding of biological relevant signals, and determination of major nodes in the pathway-network which can then can be studied in detail in which experimental design are supported by the in silico models.

Publications