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Radiation is essential to malignant glioma and glioblastoma treatment. However, the prognosis of glioblastoma remains poor with a median survival of 15 months. This is partly due to the heterogeneous radiosensitivity among patients. If we have a mechanism-based model that can make dynamic predictions, it has the potential to guide and optimize the treatment on a patient-specific basis. The purpose of this dissertation is to develop and validate a computational-experimental model that explicitly incorporates underlying radiobiology as well as making accurate predictions of the radiation response of glioma cells. Specifically, we first propose a mathematical model to a single dose of radiation that incorporates DNA repair and cell death pathways and validate it under eight different doses from 2 Gy to 16 Gy via microscopy in vitro. We then extend this model to fractionated treatment and validate it with six different fractionation schemes using total doses of either 16 Gy or 20 Gy. Finally, we propose a data assimilation framework that will individualize the prediction based on the observations of individual replicates, which further improves the prediction accuracy. We present a full story of how developing a mechanism-based experiment-driven mathematical model can assist us in characterizing and predicting radiation response, which could eventually, be used to optimize the treatment schedule.