Markov Model of Nav1.6 Voltage Gated Sodium Channel
Location
Medical Education Building, LSUHSC-NO
Presentation Date
10-10-2019 10:00 AM
End Date
10-10-2019 12:00 PM
Description
My project was motivated by previous work in Dr. Canavier’s lab using a multicompartmental computational model of a pyramidal cells in the CA1 region of the hippocampus. We are trying to create a biophysically detailed model of these cells in order to better understand how they function as place cells that fire action potentials preferentially in certain locations. Previously, in our model we had both a transient sodium channel responsible for the action potential upstroke, and a persistent sodium channel that is active during the interspike interval during repetitive spiking. The persistent sodium current is located primary on and near the soma. The transient current is located everywhere in the model, but slow inactivation of this channel is more prominent distally. We hypothesize that this variability is due to a gradient in the properties of the Nav1.6 channel, a voltage-gated sodium channel coded by the SCN8A gene. These channels were originally modeled separately by Hodgkin Huxley equations; the transient sodium channel has three activation gates and two inactivation gates, whereas the persistent has only three activation gates. This formulation ignores the state dependence of gating, as well as the fact that the persistent current is not mediated by a separate channel. I found that a single Markov Model that can exhibit both a persistent current and slow inactivation. A Markov Model assumes different states in which the likelihood of transitioning to another state is dependent on the current state. I implemented a Markov Model with one open, two closed and two inactivated states in the NEURON simulation package. I adjusted the 10 reaction rates to obtain parameter values for models that exhibit different degrees of persistent current and/or slow inactivation. This will enable us to vary the properties of this current as a function of distance in a spatially distributed, morphologically realistic model neuron.
Recommended Citation
Upchurch, Caroline M., "Markov Model of Nav1.6 Voltage Gated Sodium Channel" (2019). Medical Student Research Poster Symposium. 42.
https://digitalscholar.lsuhsc.edu/sommrd/2019/posters/42
Markov Model of Nav1.6 Voltage Gated Sodium Channel
Medical Education Building, LSUHSC-NO
My project was motivated by previous work in Dr. Canavier’s lab using a multicompartmental computational model of a pyramidal cells in the CA1 region of the hippocampus. We are trying to create a biophysically detailed model of these cells in order to better understand how they function as place cells that fire action potentials preferentially in certain locations. Previously, in our model we had both a transient sodium channel responsible for the action potential upstroke, and a persistent sodium channel that is active during the interspike interval during repetitive spiking. The persistent sodium current is located primary on and near the soma. The transient current is located everywhere in the model, but slow inactivation of this channel is more prominent distally. We hypothesize that this variability is due to a gradient in the properties of the Nav1.6 channel, a voltage-gated sodium channel coded by the SCN8A gene. These channels were originally modeled separately by Hodgkin Huxley equations; the transient sodium channel has three activation gates and two inactivation gates, whereas the persistent has only three activation gates. This formulation ignores the state dependence of gating, as well as the fact that the persistent current is not mediated by a separate channel. I found that a single Markov Model that can exhibit both a persistent current and slow inactivation. A Markov Model assumes different states in which the likelihood of transitioning to another state is dependent on the current state. I implemented a Markov Model with one open, two closed and two inactivated states in the NEURON simulation package. I adjusted the 10 reaction rates to obtain parameter values for models that exhibit different degrees of persistent current and/or slow inactivation. This will enable us to vary the properties of this current as a function of distance in a spatially distributed, morphologically realistic model neuron.
Comments
Mentor: Dr. Carmen Canavier (Department of Cell Biology and Anatomy)