Data-Driven Multiscale Modeling and Model Reduction Techniques in Biomedicine: Bridging Scales and Complexity
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Haralampos Hatzikirou
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Khalifa University
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United Arab Emirates
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Dimitrios Goussis
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Khalifa University
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United Arab Emirates
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Nikolaos Kavallaris
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Karlstads universitet
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Sweden
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Introduction:
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Biomedical research is increasingly dealing with complex data sets and biological systems that operate across multiple scales, from molecular to organismal. Data-driven multiscale modeling and model reduction methods offer powerful tools to integrate heterogeneous data types, reduce complexity, and provide insights into the underlying mechanisms of health and disease. These approaches are critical for the development of personalized medicine, efficient drug discovery, and the design of novel therapeutic interventions.
Themes:
Foundations and Innovations in Multiscale Modeling: Presentations on the latest theoretical developments, computational frameworks, and algorithms for multiscale modeling in biomedicine. This theme will cover both data-driven and mechanistic approaches to bridging scales in biological systems.
Advances in Multiscale Analysis and Model Reduction Techniques: Focus on the development and application of multiscale modeling and model reduction techniques to address the complexity of biomedical systems, facilitate computational efficiency, and extract meaningful insights.
Data-Driven Modeling in Biomedicine: Exploration of how applied mathematicians and computational scientists are leveraging big data, from genomic sequences to clinical trials, to inform and refine biomedical models.
Bridging Theory and Practice: Case studies demonstrating the successful translation of theoretical models into practical applications in diagnostics, drug development, and personalized medicine.
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