MULTIscale modeling with applications in QUANTitative bioscience

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BCAM principal investigator: Luca Gerardo-Giorda
Reference: RTI2018-093416-B-I00
Coordinator: BCAM - Basque Center for Applied Mathematics
Duration: 2019 - 2020
Funding agency: MINECO - Projects R&D&i - Challenges 2018
Type: National Project
Status: Closed


The proposed project stems from methodological and practical challenges emerged from the collaboration between the PI and medical doctors and biologists in the last 4 years. From the methodological standpoint, we aim at: a) devising robust temporal multiscale models to describe the impaired metabolic dynamic of the pancreatic beta cells in monogenic diabetes, and the reprogrammed metabolic dynamic occurring in cancer cells; b) devise a reliable inverse problem methodology to assimilate data and estimate the parameters for these multiscale models; c) devise robust UQ techniques to assess clinically relevant QoIs. From the practical standpoint, we will: a) studying the effect of space-fractional models in the description of cardiac electrophysiology to surpass the unrealistic hypothesis of tissue homogeneity that underlies the existing Bidomain and Monodomain model; b) assess the correlation between cortical spreading depression and migraine by comparing cohort of patients and by performing individual case studies on patients suffering from migraine aura; c) support the design of new strategies in cardiac radiofrequency ablation. The proposed research will foster the development of new knowledge in the field of multiscale simulation, fractional modeling, inverse problems and uncertainty quantification, from both the theoretical and the computationally standpoint. At the same time, it will have provided neurologists with a reliable tool to guide interventional strategies in the attempt to stop migraine, and cardiologists with a robust and efficient tool to test safety and efficiency of new strategies in catheter cardiac ablation. The successful development of a predictive computational model requires a mix of competences, from mathematical and numerical analysis to statistics, that are encompassed by the research team of the proposed research. Most important, interdisciplinary collaborations are fundamental to develop model that are applicable to real life problems. In this respect, the project will largely benefit from existing collaborations of the PI with cardiologists, neurologists, and biologists.

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