Speaker
Description
Complex disease mechanisms in medical research are often inferred from limited patient samples, posing fundamental challenges in capturing inter-individual variability and selecting optimal therapeutic strategies. To address this limitation, I propose a new methodological framework, termed a multi-omics methodological framework, that systematically integrates mechanistic mathematical models with spatial transcriptomics data from patient biopsy samples, clinical trial outcomes, and virtual patients. Through the iterative integration of in silico simulations and data analysis based on the pathophysiology of virtual patients, this approach enables systematic analysis of disease mechanisms, therapeutic effects, and biomarker identification. As an application, I focus on inflammatory skin diseases to demonstrate how this framework uncovers latent structures underlying disease heterogeneity, particularly in terms of immune regulatory dynamics, and links them to variability in therapeutic responses.
