| Abstract: |
| Glioblastoma (GBM) is a highly aggressive brain tumor characterized by extensive cellular heterogeneity and plasticity, which underlie its resistance to therapy and poor prognosis. Understanding and predicting the dynamic trajectories of GBM cell states is crucial for designing effective interventions. We propose a low-dimensional microscopic model driven by Levy noise that aims to capture the dynamics of gene regulation and transitions between phenotypic cell states. By formulating a stochastic optimal-control problem, we aim to find a suitable profile for the cell transcription factors that can drive cell-state transitions towards a particular desired state. Following this, we establish a macroscopic model for the system and propose a framework for integrating high-throughput single-cell data, thus offering a powerful tool for understanding GBM dynamics and guiding therapeutic development. |
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