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Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and 120∼330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks. 

Introduction: RE1-silencing transcription factor (REST) silences neuronal differentiation genes. Its overexpression in an aggressive subset of gliomas is believed to support the enhanced tumor-initiating and self-renewal capacities of glioblastoma cancer stem cells (GSCs). Therefore, REST knockdown is hypothesized to inhibit tumor growth and recurrence. Because REST, as a large protein, is difficult to target directly with small molecules, our study focuses on knocking down REST by inhibiting one of its regulatory enzymes, small C-terminal domain phosphatase 1 (SCP1). Dephosphorylation of REST by SCP1 protects the former from degradation; consequently, SCP1 inhibition with an experimental drug, T62, is expected to reduce REST protein levels. This REST knockdown is hypothesized to induce the expression of neuronal differentiation genes, thereby forcing differentiation of GSCs and making them more vulnerable to standard treatments. We begin our study by validating patient-derived GSC lines and subsequently testing the efficacy of T62 drug in these cells. Our work supports an effort to understand various molecular pathologies of GBM and its intrinsic GSCs in order to develop novel therapeutic strategies.