Data analysis, data integration and rule-based modeling of food allergy

• Applicants: Dr. Priyanka Banerjee, Ph.D.; Professorin Dr. Sofia Forslund-Startceva; Privatdozentin Dr. Stephanie Roll, Ph.D.
• Subject Area: Clinical Immunology and Allergology
• Term: since 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 409525714

Project C2 is the data science support project of this clinical research unit. It aims to facilitate each aspect of the collaboration, with emphasis on the study design and analysis of outcomes, integrative analysis of high-dimensional / -omics data from the mechanistic projects, the connection of the studies to results of bottom-up modeling (e.g. agent-based, starting from a conceptual model of interconnected parts rather than directly from high-throughput data) of immune response using artificial intelligence, as well as an overall combination of collected data for a comprehensive food allergy risk inference model.Being structured into several work packages, C2 has the following aims:(i) methodological support and statistical data analysis for A1 and A2, i.e. for the RCTs, the cohort study, and their extension studies, as well as any accompanying analyses that emerge during the funding time; (ii) to assess the extent of microbiotal and other mediation factors on food allergy risk using systems biology approaches, i.e. to identify patient-specific but also allergen-specific factors, which will help to understand the mechanisms of food allergy development and finally to predict clinical reactivity to a certain protein for each individual, yielding good candidates for translation into personalized medicine tools, especially drawing from microbiome data generated in B1 and epigenetic and gene expression data generated in B2;(iii) the prediction of Adverse Outcome Pathways (AOPs) for food allergy, by collecting and organizing information relevant to an adverse outcome at different levels of biological organization, the development of relevant predictive test methods and approaches, as well as the contextualization of the results across a diverse range of biological mechanisms and immunotoxic endpoints. In addition, an assay system that allows for monitoring and modifying of APC polarization by different signaling molecules will be developed in B4 to validate the predicted outcomes from C2.(iv) to establish and deploy methods for computational prediction of food allergy based on a combination of collected clinical and –omics data, and to combine data into a single composite food allergy risk inference model, to be robustly assessed under cross-validation.

• DFG Programme: Clinical Research Units
• Subproject of: KFO 339: Food Allergy and Tolerance (Food@)