Experimentally Constrained Mechanistic and Data-Driven Models for Simulating NMDA Receptor Dynamics.

Area of Science:

• Neuroscience
• Computational Biology
• Biophysics

Background:

• N-methyl-D-aspartate receptors (NMDA-Rs) are vital for synaptic plasticity, learning, and memory.
• NMDA-R dysfunction is linked to neurological diseases, but their complex dynamics are difficult to model.
• Accurate modeling of NMDA-R dynamics is essential for understanding brain function and dysfunction.

Purpose of the Study:

• To develop and calibrate computationally efficient models of GluN1/GluN2A NMDA-R dynamics.
• To create a nine-state kinetic model and a reduced-footprint look-up table model.
• To ensure models accurately replicate experimental findings and complex receptor behaviors.

Main Methods:

• Elaboration and calibration of experimentally constrained computational models.
• Optimization of a nine-state kinetic model using particle swarm optimization.
• Development of a look-up table synapse model trained on kinetic model output.

Main Results:

• The optimized kinetic model accurately reproduced experimental data, including frequency-dependent potentiation and glutamate-induced temporal responses.
• The look-up table synapse model closely mimicked the dynamics of the nine-state kinetic model.
• Both models demonstrated high fidelity in reproducing NMDA-R dynamics.

Conclusions:

• Developed models serve as accurate alternatives for simulating NMDA-R dynamics.
• The look-up table model provides significant computational efficiency.
• This efficient implementation is ideal for integration into large-scale neuronal network models.