Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis
Background: The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms. Methods: We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms. Results: Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation data for a comorbidity analysis between AD and type 2 diabetes mellitus. Conclusion: The two computable, literature-based models introduced here provide a powerful framework for the generation and validation of rational, testable hypotheses across disease areas.