Speaker
Description
Tumor progression is shaped by a complex interplay between genomic heterogeneity, biophysical interactions, and the tissue microenvironment. Computational models of tumor growth have historically struggled to incorporate the full richness of patient-specific data — from genomics and transcriptomics to medical imaging and tissue mechanics. Here, I will present a multi-scale computational approach designed to bridge this gap. The centerpiece is a stochastic state-space modeling framework that discretizes the tissue microenvironment into spatially resolved voxels characterized by resource availability, mechanical properties, vascular state, and clonal composition of tumor cells. Multi-modal patient data — medical imaging, bulk genomics, and digital pathology — parameterize and personalize model behavior, while cell proliferation, death, migration, and metabolic state are governed by microenvironmental conditions. Applied to glioblastoma, the framework integrates brain-scale atlases of oxygen, glucose, and tissue stiffness with patient MRI and whole-genome sequencing to simulate how ploidy-dependent resource sensitivity shapes clonal competition and spatial patterns of tumor recurrence. To complement this meso- to macroscale approach, I will briefly introduce a pipeline connecting RNA sequencing data to biophysical models of single-cell migration through cytoskeletal signaling networks — a path toward directly informing cell migration parameters from patient transcriptomic data.
