Electroenzymatic glutamate microbiosensors have proven useful for enabling local measurements of concentration transients in the brain, but due to the diffusional and kinetic resistances of the biosensor’s permselective and immobilized enzyme coatings, the sensor response cannot generally be assumed to be a true representation of actual glutamate signals. To model the biosensor’s ability to track changing concentrations and to optimize its ability to do so, a mathematical model describing transport through sensor coatings, ping-pong enzyme kinetics, and H2O2 electrooxidation kinetics was constructed and solved using a finite element solver (COMSOL). Optimization studies showed that reducing permselective Nafion thickness to 0.1 µm from ~10 µm, and the enzyme layer thickness to 1 µm from ~20 µm, could increase sensitivity from 45 to 474 nA/µM/cm2 and bring response times down from ~0.6 s to 10 ms. Simulated biosensors with ms response times were also shown to accurately detect glutamate concentration peaks with standard deviations as small as 0.02-0.25 s, while slower simulated sensors reflective of the state-of-the-art underestimated the glutamate in such peaks by as much as 80%. The modeling results also showed that changing the relative proportions of glutamate oxidase and bovine serum albumin in the crosslinked enzyme layer had little effect on biosensor performance unless the enzyme layer was &lt;5 µm thick. Finally oxygen depletion within the enzyme layer was predicted to occur only at high (&gt;5 mM) glutamate concentrations, which explains experimental observations of increased apparent Km of the immobilized enzyme. These modeling results suggest that the performance of electroenzymatic glutamate biosensors can be improved dramatically, which will contribute to better resolution of glutamate transients in vivo and better correlation of glutamate signaling with observed behavior.

Mathematical model to optimize electroenzymatic microbiosensors for the monitoring of glutamate signaling in the brain

253rd National Meeting (2017)

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