Glioblastomas (GBMs) are an aggressive glioma subtype that account for the majority of primary brain tumors. Recurrence is almost inevitable. Physiology-based computational models have the potential to serve as tools to investigate mechanics and transport in the tumor microenvironment. Model insights can support clinical decision-making and optimize treatment. GBMs are characterized as highly vascularized, soft tissues. Abnormal vessel formation allows for plasma proteins to leak into the interstitial (pore) space, increasing elevated interstitial fluid volume and pressure. High tumor interstitial fluid pressure (TIFP) and mass growth generate mechanical stresses in the tumor and surrounding brain tissue. Our lab has developed a non-linear biphasic model which considers the atypical perfusion characteristics and coupled solid-fluid behavior in an animal model of GBM [1, 2]. Fluid generation and flow are modeled by Starling’s law and Darcy’s law, respectively. In vivo measures of TIFP and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived estimates of tissue porosity and vascular leakiness were incorporated [2, 3]. Current efforts are focused on examining the impact of radiation-induced changes in perfusion on predictions of stress and flow field. TIFP and tissue porosity were significantly lower post-radiation. These findings led to predicted lower radial fluid pressure, velocity magnitudes, and tensile stress for the irradiated group. Ongoing studies include scaling up the model for human datasets to explore how subject-specific stress and flow fields influence treatment efficacy and response, with the aim of improving patients’ quality of life.