A significant challenge in treating some aggressive cancers and neurological diseases is the lack of understanding regarding the spatial-temporal molecular heterogeneity of the diseased tissue/organ. Molecular characteristics and interaction vary significantly across different spatial sub-units of the tissue/organ. The spatial pattern also changes over time as the disease progresses. The objective of this project is to develop a suite of new statistical models for inverse mapping/estimation of the spatial-temporal heterogeneity of molecular biomarkers from multimodality image phenotype. We propose a novel modeling framework that integrates data-driven and biological-principle-driven mechanistic models, and meanwhile fuses global-scale image data and sparsely-sampled local biopsy measurements. This framework embraces modeling approaches to characterize both spatial heterogeneity and temporal dynamics of the disease. The proposed models will be validated in two applications: glioblastoma and Alzheimer’s Disease. This project is expected to generate significant insight for unraveling the complex biological systems underlying these diseases and provide the groundwork for new treatment intervention. Additionally, the proposed modeling framework integrates statistical and bio-mechanistic models, which bridges two traditionally separate research fields together.