Simulations conducted with a detailed model of glutamate biosensor performance describe the observed sensor performance well, illustrate the limits of sensor performance, and suggest a path toward sensor optimization. Glutamate is the most important excitatory neurotransmitter in the brain, and electroenzymatic sensors have emerged as a useful tool for the monitoring of glutamate signaling in vivo. However, the utility of these sensors currently is limited by their sensitivity and response time. A mathematical model of a typical glutamate biosensor consisting of a Pt electrode coated with a permselective polymer film and a top layer of cross-linked glutamate oxidase has been constructed in terms of differential material balances on glutamate, H₂O₂, and O₂ in one spatial dimension. Simulations suggest that reducing thicknesses of the permselective polymer and enzyme layers can increase sensitivity ∼6-fold and reduce response time ∼7-fold, and thereby improve resolution of transient glutamate signals. At currently employed enzyme layer thicknesses, both intrinsic enzyme kinetics and enzyme deactivation likely are masked by mass transfer. However, O₂-dependence studies show essentially no reduction in signal at the lowest anticipated O₂ concentrations for expected glutamate concentrations in the brain and that O₂ transport limitations in vitro are anticipated only at glutamate concentrations in the mM range. Finally, the limitations of current biosensors in monitoring glutamate transients is simulated and used to illustrate the need for optimized biosensors to report glutamate signaling accurately on a subsecond time scale. This work demonstrates how a detailed model can be used to guide optimization of electroenzymatic sensors similar to that for glutamate and to ensure appropriate interpretation of data gathered using such biosensors.

中文翻译：

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酶促谷氨酸生物传感器的详细模型有助于传感器优化和体内应用

用谷氨酸生物传感器性能的详细模型进行的仿真很好地描述了观察到的传感器性能，说明了传感器性能的局限性，并提出了实现传感器优化的途径。谷氨酸是脑中最重要的兴奋性神经递质，并且电子酶传感器已成为监测体内谷氨酸信号的有用工具。但是，这些传感器的实用性目前受到其灵敏度和响应时间的限制。根据在谷氨酸，H ₂ O ₂和O上的不同物质平衡，已经构建了一个典型的谷氨酸生物传感器的数学模型，该传感器由涂覆有选择性渗透聚合物膜的Pt电极和交联的谷氨酸氧化酶的顶层组成。在一个空间维度上为₂。模拟表明，降低渗透选择性聚合物和酶层的厚度可以使灵敏度提高约6倍，并缩短响应时间约7倍，从而提高瞬时谷氨酸信号的分辨率。在目前采用的酶层厚度下，固有的酶动力学和酶失活都可能被传质所掩盖。但是，O ₂依赖性研究表明，对于大脑中预期的谷氨酸盐浓度，在最低的预期O ₂浓度下，信号基本上没有减少，并且O ₂的体外运输限制仅在mM范围内的谷氨酸浓度下才可预期。最后，模拟了当前生物传感器在监测谷氨酸瞬时过程中的局限性，并用于说明需要优化的生物传感器以在亚秒级的时间范围内准确报告谷氨酸信号。这项工作演示了如何使用详细模型指导类似于谷氨酸的电酶传感器的优化，并确保对使用此类生物传感器收集的数据进行适当的解释。