Abstract
Because of its highly branched dendrite, the Purkinje neuron requires significant computational resources if coupled electrical and biochemical activity are to be simulated. To address this challenge, we developed a scheme for reducing the geometric complexity; while preserving the essential features of activity in both the soma and a remote dendritic spine. We merged our previously published biochemical model of calcium dynamics and lipid signaling in the Purkinje neuron, developed in the Virtual Cell modeling and simulation environment, with an electrophysiological model based on a Purkinje neuron model available in NEURON. A novel reduction method was applied to the Purkinje neuron geometry to obtain a model with fewer compartments that is tractable in Virtual Cell. Most of the dendritic tree was subject to reduction, but we retained the neuron’s explicit electrical and geometric features along a specified path from spine to soma. Further, unlike previous simplification methods, the dendrites that branch off along the preserved explicit path are retained as reduced branches. We conserved axial resistivity and adjusted passive properties and active channel conductances for the reduction in surface area, and cytosolic calcium for the reduction in volume. Rallpacks are used to validate the reduction algorithm and show that it can be generalized to other complex neuronal geometries. For the Purkinje cell, we found that current injections at the soma were able to produce similar trains of action potentials and membrane potential propagation in the full and reduced models in NEURON; the reduced model produces identical spiking patterns in NEURON and Virtual Cell. Importantly, our reduced model can simulate communication between the soma and a distal spine; an alpha function applied at the spine to represent synaptic stimulation gave similar results in the full and reduced models for potential changes associated with both the spine and the soma. Finally, we combined phosphoinositol signaling and electrophysiology in the reduced model in Virtual Cell. Thus, a strategy has been developed to combine electrophysiology and biochemistry as a step toward merging neuronal and systems biology modeling.
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Acknowledgements
We thank Fei Gao and Anuradha Lakshminarayana for help and support in various aspects of this work. We are also pleased to acknowledge Dr. Corey Acker, Dr. James Watras, Dr. Ann Cowan, and other members of the R. D. Berlin Center for Cell Analysis & Modeling at the University of Connecticut Health Center for helpful discussions.
This research was supported by grants R01 MH086638, P41 RR013186 and U54 RR022232 from the National Institutes of Health.
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Brown, SA., Moraru, I.I., Schaff, J.C. et al. Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling. J Comput Neurosci 31, 385–400 (2011). https://doi.org/10.1007/s10827-011-0317-0
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DOI: https://doi.org/10.1007/s10827-011-0317-0