Through discussions, break out sessions and working groups participants will:
- Develop a conceptual map of a core immune system model that encodes the basic “infrastructure” underlying most or all of the immune system functions in health and disease. This will involve a list of immune cell types and a basic intracellular network of pathways and mechanisms capturing gene regulation, signaling, and metabolism.
- Identify two or three specific use cases for an IDT that are suitable as a focus for initial projects. These could be a specific disease that involves the immune system, such as a particular viral infection in a particular patient, or it could be a framework for drug repurposing studies using a custom-designed synthetic patient population. For each use case, identify the additions to the core immune system required.
- Identify modeling approaches and software that can be used to build IDTs in different scales.
- Systems biology software: Systems biology software can be used to build computational models of biological systems, such as the immune system. These models can be based on data from various sources, such as gene expression data, protein levels, and pathway analysis.
- Data integration software: Data integration software can be used to integrate data from multiple sources and formats, such as electronic health records, genomics data, and patient-reported outcomes.
- Machine learning algorithms: Machine learning algorithms can be used to analyze data from the mechanistic model and identify patterns and relationships between different variables. This can be useful for predicting the effects of different treatments.
- Visualization software: Visualization software can be used to display and analyze the mechanistic model and its results.
- Determine what needs to be done to validate the expanded model, and how it will be personalized. Determine which data are needed and how to acquire them.
