The tripartite synapse during metabolic stress

z项目将最先进的实验方法与先进的计算模型结合起来。基于在RU中获得的实验结果，我们生成了代谢应激下三方突触的生物物理模型。该模型包括一个突触前神经元和一个嵌入有限细胞外空间（ECS）的星形胶质细胞。各种跨膜电流及其能量依赖关系表示为用实验数据校准的耦合微分方程。该模型忠实地再现了关键的实验观察，包括离子、体积和电生理动力学。我们发现，对瞬态能量剥夺的反应是双稳定的：神经元可以从瞬态ATP耗尽中恢复，并随着突触传递的恢复而重新极化，也可以保持在去极化状态，这取决于NKA泵的强度和ECS的大小。在第二个资助期，我们将通过纳入重要的新元素来扩展该模型，包括Na+依赖的酸碱转运蛋白（NHE1和NBCe1）和星形胶质细胞和神经元的动态水渗透性，以探索体积调节。将增加内体室以更详细地描述细胞内离子分布，并增加突触后室以更真实地模拟能量剥夺期间的突触传递失败。此外，我们将实现谷氨酸-谷氨酰胺，并包括线粒体功能。为了模拟相互作用神经元的能量依赖活动，我们将添加抑制性突触，然后根据我们的生物物理模型用神经团来描述种群活动。这种神经质量模型使我们能够模拟脑节律（EEG）及其在代谢应激下的变化。校准新的实验数据并使用先进的分岔分析，我们将深入了解神经网络中抑制性和兴奋性突触的选择性脆弱性，包括三方突触的各种能量依赖过程。我们的计算模型还提供了一个测试计算机干预的平台，从而为新的实验产生假设。最终，我们的模型和联盟的实验数据将促进我们对许多ATP生成减少的神经系统疾病的临床现象的理解，包括中风、缺氧缺血性脑病、癫痫和线粒体病。

The Z-project connects state-of-the-art experimental approaches to advanced computational modelling. Based on experimental findings obtained in the RU, we have generated a biophysical model of the tripartite synapse under metabolic stress. The model includes a presynaptic neuron and an astrocyte embedded in a finite extracellular space (ECS). The various transmembrane currents and their energy dependency are expressed as coupled differential equations calibrated with experimental data. The model faithfully reproduces key experimental observations, including ion, volume and electrophysiological dynamics. We found that the response to transient energy deprivation is bi-stable: neurons can either recover from transient ATP depletion and repolarise with the restoration of synaptic transmission or remain in a depolarised state, depending on NKA pump strength and the size of the ECS. In the second Funding Period, we will extend this model by incorporating important new elements, including Na+-dependent acid-base transporters (NHE1 and NBCe1) and dynamic water permeabilities for astrocytes and neurons to explore volume regulation. Endosomal compartments will be added for a more detailed description of intracellular ion distributions and a postsynaptic compartment to simulate synaptic transmission failure during energy deprivation more faithfully. Moreover, we will implement the glutamate-glutamine and include mitochondrial function. To simulate the energy-dependent activity of interacting neurons, we will add inhibitory synapses and subsequently describe the population activity with a neural mass based on our biophysical model. This neural mass model enables us to simulate brain rhythms (EEG) and their changes during metabolic stress. Calibrating to new experimental data and using advanced bifurcation analysis, we will gain insight into the selective vulnerability of inhibitory and excitatory synapses, including the various energy-dependent processes at the tripartite synapse, within a neural network. Our computational model also provides a platform to test interventions in silico, thus generating hypotheses for new experiments. Ultimately, our model and the consortiums' experimental data will advance our understanding of the clinical phenomenology of many neurological diseases where ATP generation is reduced, including stroke, hypoxic-ischemic encephalopathy, seizures and mitochondriopathies.