Grant: Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives

Digital twin technology is used to model, explain, understand and predict behavior of complex systems in order to make their performance more reliable or to provide an in silico platform for high throughput testing of the effect of different interventions. Models combined under a digital twin umbrella can also be used to study mechanistic phenomena of a disease. While there are extensive data using genetically and environmentally manipulated laboratory mice to model human disease, these data are study-dependent and have yet to interrogate how metabolism dictates outcome and modulates genetic susceptibility. Moreover, there are no in silico models that combine the multiple behavioral outcomes of these in vivo modelling data with the metabolome. The lack of a murine digital twin is a major missing link in intelligent design of therapeutic strategies for complex diseases. The driving hypothesis of this project is that a digital mouse developed through optimized AI approaches and extensive, available biological data can provide a framework to support the design of evidence-based therapies for neurodegenerative diseases. The main outcome of this project will be an in silico reconstruction and simulation of the dependence of behavior, as a measure of neurodegeneration, on the lipidome and its application in corrective intervention development. The proposed digital mouse will be built using novel AI algorithms and unique, extensive, and longitudinal murine datasets (from birth to death) made available through collaboration.