Determinants of fast calcium dynamics in dendritic spines and dendrites (Cornelisse et al. 2007)

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"... Calcium influx time course and calcium extrusion rate were both in the same range for spines and dendrites when fitted with a dynamic multi-compartment model that included calcium binding kinetics and diffusion. In a subsequent analysis we used this model to investigate which parameters are critical determinants in spine calcium dynamics. The model confirmed the experimental findings: a higher SVR (surface-to-volume ratio) is not sufficient by itself to explain the faster rise time kinetics in spines, but only when paired with a lower buffer capacity in spines. Simulations at zero calcium-dye conditions show that calmodulin is more efficiently activated in spines, which indicates that spine morphology and buffering conditions in neocortical spines favor synaptic plasticity. ..."
1 . Cornelisse LN, van Elburg RA, Meredith RM, Yuste R, Mansvelder HD (2007) High speed two-photon imaging of calcium dynamics in dendritic spines: consequences for spine calcium kinetics and buffer capacity. PLoS One 2:e1073 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
Gap Junctions:
Simulation Environment: CalC Calcium Calculator;
Model Concept(s): Calcium dynamics;
Implementer(s): van Elburg, Ronald A.J. [R.van.Elburg at];
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
% Title: 	Calcium Signals in Small Structures
% Filename:	CaSignal_SVREndoSphereTraces.par
% Author:   Ronald van Elburg
% Associated Paper:
% Cornelisse LN, van Elburg RAJ, Meredith RM, Yuste R, Mansvelder HD (2007) 
% High Speed Two-Photon Imaging of Calcium Dynamics in Dendritic Spines: 
% Consequences for Spine Calcium Kinetics and Buffer Capacity. 
% PLoS ONE 2(10): e1073 doi:10.1371/journal.pone.0001073
Structure			= Sphere_Structure% Make it a sphere
R_Structure			= 3/Sigma		% Radius of the simulated structure: Cylinder or Sphere (0.57 um)

path = ".\"   		% If running under Windows, specify here the path to the
                           			% directory containing the script imported below
file = path "CaSignal_main.par"

include file               			% Import the simulation parameters from the main script

% Auxilary variables for monitoring concentrations at different distances from the membrane

	NoOfSteps = 6    % number of shells in the output (NOT IN THE SIMULATION, THERE THE GRIDSIZE DEFINES THE COMPARTMENTS)
	Ca1 := Ca[R1k] ; Dye1 := Dye[R1k] 	; BndDye1 := Total_Dye-Dye[R1k] 	;EndoB1 := EndogenousBuffer	[R1k]
	Ca2 := Ca[R2k] ; Dye2 := Dye[R2k] 	; BndDye2 := Total_Dye-Dye[R2k] 	;EndoB2 := EndogenousBuffer	[R2k]
	Ca3 := Ca[R3k] ; Dye3 := Dye[R3k]  	; BndDye3 := Total_Dye-Dye[R3k]  	;EndoB3 := EndogenousBuffer	[R3k]
	Ca4 := Ca[R4k] ; Dye4 := Dye[R4k] 	; BndDye4 := Total_Dye-Dye[R4k] 	;EndoB4 := EndogenousBuffer	[R4k]
	Ca5 := Ca[R5k] ; Dye5 := Dye[R5k] 	; BndDye5 := Total_Dye-Dye[R5k] 	;EndoB5 := EndogenousBuffer	[R5k]
	Ca6 := Ca[R6k] ; Dye6 := Dye[R6k] 	; BndDye6 := Total_Dye-Dye[R6k] 	;EndoB6 := EndogenousBuffer	[R6k]
	CaAverage:=Ca[] ; DyeAverage:=Dye[] ; BndDyeAverage:=Total_Dye-Dye[] 	;EndoBAverage := EndogenousBuffer	[]

% Exporting the variables defined above to file


  plot point.mute  CaBoundary "Output\Exp""Exp""\CSE""Exp""Geom""_CaBoundary_""LoopVar""_""LoopVar2"
	plot point.mute  Ca1 "Output\Exp""Exp""\CSE""Exp""Geom""_Ca1_""LoopVar""_""LoopVar2"
	plot point.mute  Ca2 "Output\Exp""Exp""\CSE""Exp""Geom""_Ca2_""LoopVar""_""LoopVar2"
	plot point.mute  Ca3 "Output\Exp""Exp""\CSE""Exp""Geom""_Ca3_""LoopVar""_""LoopVar2"
	plot point.mute  Ca4 "Output\Exp""Exp""\CSE""Exp""Geom""_Ca4_""LoopVar""_""LoopVar2"
	plot point.mute  Ca5 "Output\Exp""Exp""\CSE""Exp""Geom""_Ca5_""LoopVar""_""LoopVar2"
	plot point.mute  Ca6 "Output\Exp""Exp""\CSE""Exp""Geom""_Ca6_""LoopVar""_""LoopVar2"
	plot point.mute  CaAverage "Output\Exp""Exp""\CSE""Exp""Geom""_CaAverage_""LoopVar""_""LoopVar2"
	plot point.mute  Dye1 "Output\Exp""Exp""\CSE""Exp""Geom""_Dye1_""LoopVar""_""LoopVar2"
	plot point.mute  Dye2 "Output\Exp""Exp""\CSE""Exp""Geom""_Dye2_""LoopVar""_""LoopVar2"
	plot point.mute  Dye3 "Output\Exp""Exp""\CSE""Exp""Geom""_Dye3_""LoopVar""_""LoopVar2"
	plot point.mute  Dye4 "Output\Exp""Exp""\CSE""Exp""Geom""_Dye4_""LoopVar""_""LoopVar2"
	plot point.mute  Dye5 "Output\Exp""Exp""\CSE""Exp""Geom""_Dye5_""LoopVar""_""LoopVar2"
	plot point.mute  Dye6 "Output\Exp""Exp""\CSE""Exp""Geom""_Dye6_""LoopVar""_""LoopVar2"
	plot point.mute  DyeAverage "Output\Exp""Exp""\CSE""Exp""Geom""_DyeAverage_""LoopVar""_""LoopVar2"
	plot point.mute  EndoB1 "Output\Exp""Exp""\CSE""Exp""Geom""_EndoB1_""LoopVar""_""LoopVar2"
	plot point.mute  EndoB2 "Output\Exp""Exp""\CSE""Exp""Geom""_EndoB2_""LoopVar""_""LoopVar2"
	plot point.mute  EndoB3 "Output\Exp""Exp""\CSE""Exp""Geom""_EndoB3_""LoopVar""_""LoopVar2"
	plot point.mute  EndoB4 "Output\Exp""Exp""\CSE""Exp""Geom""_EndoB4_""LoopVar""_""LoopVar2"
	plot point.mute  EndoB5 "Output\Exp""Exp""\CSE""Exp""Geom""_EndoB5_""LoopVar""_""LoopVar2"
	plot point.mute  EndoB6 "Output\Exp""Exp""\CSE""Exp""Geom""_EndoB6_""LoopVar""_""LoopVar2"
	plot point.mute  EndoBAverage "Output\Exp""Exp""\CSE""Exp""Geom""_EndoBAverage_""LoopVar""_""LoopVar2"

% Parameter variation
	for Total_EndogenousBuffer = 25 to 1000 step 975
	for Sigma   = 0.25  to 10 step 9.75

% The adaptive integration method fails for the fast calcium change
% to overcome this problem we run the first 20 ms with a fixed timestep 
% of 0.001 ms, then after the fast changes we switch to the adaptive method 
% for optimal performance.

Run  20.0  1.0e-3 ; current CalciumCurrent

Run  adaptive 480.0  1.0e-3 accuracy; current CalciumCurrent

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