Speakers
Description
Quantitative experimental methods in the life sciences have advanced rapidly in recent years. The emergence of multi-omics, organoid systems, and in vivo live imaging has placed data at the center of modern biological discovery, driving the rapid evolution of data science for extracting biologically meaningful insights.
In parallel, mathematical modeling in the life sciences is shifting from primarily qualitative approaches toward data-driven and data-integrated paradigms. A key challenge is how to effectively fuse structure-based, mechanistic models with high-dimensional and often unstructured biological data, which is essential for achieving a deeper, system-level understanding of complex biology and medicine.
Importantly, this problem cannot be solved by conventional modeling frameworks alone. Instead, it demands a genuine and seamless integration of two mathematical sciences: mechanistic modeling grounded in biological principles, and data-centric methodologies that leverage modern statistical and computational techniques. Their full convergence is essential for next-generation discovery in the life sciences.
This mini-symposium aims to share and discuss cutting-edge research that tackles these challenges, highlighting novel frameworks, methodologies, and applications at the interface of mathematical modeling, data science, and quantitative life and medical science.
