The immune system is a large contributor in maintaining health. It is made up of a complex network of cells and organs protecting the body from infectious diseases.1 Therefore, immune disturbances play a key role in immunodeficiency, infectious diseases, cancer, obesity associated pathology, geriatrics, allergy, autoimmunity, mental disorders and toxicity in general.
Systems biology approach
Despite scientific advances, the number of useful and practical biomarkers regarding the risk and benefit balance of immune interventions remains disappointing. This fact led to a new direction to explore how network models built at multiple molecular or cellular levels are used to illuminate functional and structural relationships.
Frontiers in Immunology published the “The impact of immune interventions: A systems biology strategy for predicting adverse and beneficial immune effects” study. This immune study is aimed at showing how a systems biology approach can be used to identify immune health endpoints including hypersensitivity, autoimmunity and resistance to infection and cancer. Additionally, it ranks crucial candidate biomarkers to predict adverse and beneficial effects of immunonutrition on these endpoints.
The nutritional immune intervention was formed with literature research, database information, gene ontology, and an evaluation of the genes. Specifically, molecular and cellular dynamics involved in the mentioned immune health endpoints were selected based on a literature research. Thereafter, molecular interaction between molecules and immune health endpoints was defined by connecting their relations using database information. Additionally, a sequence of key processes was determined using gene ontology, which drives the development of immune health disturbance. Thus, an evaluation of the genes for each of the immune health endpoints was performed.
Conclusion and future studies
The study provides a promising systems biology approach to identify genes with regard to immune related disorders. It has become a scientific premise for optimizing future studies designed to find both negatively and positively correlated interactions.