This work was supported in part by the Sao Paulo State Science Foundation (FAPESP) grant #2015/50122-0 and IRTG-GRTK 1740/2, by the IBM/FAPESP grant #2016/18825-4, and by the FAPESP grant #2018/06504-4.

NOTE: Please see the Supplementary file 1 for the glossary of terms used in this protocol.
1. Install Blender, CellBlender, and MCell
NOTE: This protocol requires installation of MCell, Blender, and Cell Blender.
<ol>
	<li>Download and install the software on the MCell homepage (https://mcell.org/tutorials_iframe.html). Go to downloads on the top of the page and then follow the step-by-step instructions to download and install the software in the ...

<ol>
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W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+postsynaptic+localization+of+AMPA-type+glutamate+receptors+and+their+regulation+during+long-term+potentiation.">Mechanisms of postsynaptic localization of AMPA-type glutamate receptors and their regulation during long-term potentiation.</a> Science Signaling. 12 (562), 6889 (2019).</li><li>Nair, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-Resolution+Imaging+Reveals+That+AMPA+Receptors+Inside+Synapses+Are+Dynamically+Organized+in+Nanodomains+Regulated+by+PSD95.">Super-Resolution Imaging Reveals That AMPA Receptors Inside Synapses Are Dynamically Organized in Nanodomains Regulated by PSD95.</a> Journal of Neuroscience. 33 (32), 13204-13224 (2013).</li><li>Cz&#246;nd&#246;r, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unified+quantitative+model+of+AMPA+receptor+trafficking+at+synapses.">Unified quantitative model of AMPA receptor trafficking at synapses.</a> Proceeding of the National Academy of Sciences of the United States of America. 109 (9), 3522-3527 (2012).</li><li>Triesch, J., Vo, A. D., Hafner, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Competition+for+synaptic+building+blocks+shapes+synaptic+plasticity.">Competition for synaptic building blocks shapes synaptic plasticity.</a> eLife. 7, 37836 (2018).</li><li>Earnshaw, B. A., Bressloff, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biophysical+model+of+AMPA+receptor+trafficking+and+its+regulation+during+long-term+potentiation/long-term+depression.">Biophysical model of AMPA receptor trafficking and its regulation during long-term potentiation/long-term depression.</a> Journal of Neuroscience. 26 (47), 12362-12373 (2006).</li><li>Earnshaw, B. A., Bressloff, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+the+role+of+lateral+membrane+diffusion+in+AMPA+receptor+trafficking+along+a+spiny+dendrite.">Modeling the role of lateral membrane diffusion in AMPA receptor trafficking along a spiny dendrite.</a> Journal of Computational Neuroscience. 25 (2), 366-389 (2008).</li><li>Antunes, G., Roque, A. C., Simoes-de-Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stochastic+Induction+of+Long-Term+Potentiation+and+Long-Term+Depression.">Stochastic Induction of Long-Term Potentiation and Long-Term Depression.</a> Scientific Reports. 6, 30899 (2016).</li><li>Kotaleski, J. H., Blackwell, K. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+the+molecular+mechanisms+of+synaptic+plasticity+using+systems+biology+approaches.">Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches.</a> Nature Reviews Neuroscience. 11 (4), 239-251 (2010).</li><li>Bhalla, U. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+computation+in+neurons:+a+modeling+perspective.">Molecular computation in neurons: a modeling perspective.</a> Current Opinion in Neurobiology. 25, 31-37 (2014).</li><li>Cz&#246;nd&#246;r, K., Thoumine, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biophysical+mechanisms+regulating+AMPA+receptor+accumulation+at+synapses.">Biophysical mechanisms regulating AMPA receptor accumulation at synapses.</a> Brain Research Bulletin. 93, 57-68 (2013).</li><li>Bromer, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-term+potentiation+expands+information+content+of+hippocampal+dentate+gyrus+synapses.">Long-term potentiation expands information content of hippocampal dentate gyrus synapses.</a> Proceedings of the National Academy of Sciences of the United States of America. 115 (10), 2410-2418 (2018).</li><li>Antunes, G., Simoes-de-Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AMPA+receptor+trafficking+and+its+role+in+heterosynaptic+plasticity.">AMPA receptor trafficking and its role in heterosynaptic plasticity.</a> Scientific Reports. 8 (1), 10349 (2018).</li><li>Kerr, R. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+monte+carlo+simulation+methods+for+biological+reaction-diffusion+systems+in+solution+and+on+surfaces.">Fast monte carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces.</a> SIAM Journal on Scientific Computing. 30 (6), 3126 (2008).</li><li>Czech, J., Dittrich, M., Stiles, J. 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C., Simoes de Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+intracellular+competition+for+calcium:+kinetic+and+thermodynamic+control+of+different+molecular+modes+of+signal+decoding.">Modelling intracellular competition for calcium: kinetic and thermodynamic control of different molecular modes of signal decoding.</a> Scientific Reports. 6, 23730 (2016).</li><li>Antunes, G., Roque, A. C., Simoes-de-Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+mechanisms+of+detection+and+discrimination+of+dynamic+signals.">Molecular mechanisms of detection and discrimination of dynamic signals.</a> Scientific Reports. 8 (1), 2480 (2018).</li><li>Hoops, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=COPASI--a+COmplex+PAthway+SImulator.">COPASI--a COmplex PAthway SImulator.</a> Bioinformatics. 22 (24), 3067-3074 (2006).</li><li>Faeder, J. R., Blinov, M. L., Hlavacek, W. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rule-based+modeling+of+biochemical+systems+with+BioNetGen.">Rule-based modeling of biochemical systems with BioNetGen.</a> Methods in Molecular Biology. 500, 113-167 (2009).</li><li>Gillespie, D. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exact+stochastic+simulation+of+coupled+chemical+reactions.">Exact stochastic simulation of coupled chemical reactions.</a> Journal of Physical Chemistry. 81 (25), 21 (1977).</li><li>Anggono, V., Huganir, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+AMPA+receptor+trafficking+and+synaptic+plasticity.">Regulation of AMPA receptor trafficking and synaptic plasticity.</a> Current Opinion in Neurobiology. 22 (3), 461-469 (2012).</li><li>Matsuda, S., Launey, T., Mikawa, S., Hirai, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Disruption+of+AMPA+receptor+GluR2+clusters+following+long-term+depression+induction+in+cerebellar+Purkinje+neurons.">Disruption of AMPA receptor GluR2 clusters following long-term depression induction in cerebellar Purkinje neurons.</a> EMBO Journal. 19 (12), 2765-2774 (2000).</li><li>Ahmad, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Postsynaptic+Complexin+Controls+AMPA+Receptor+Exocytosis+during+LTP.">Postsynaptic Complexin Controls AMPA Receptor Exocytosis during LTP.</a> Neuron. 73 (2), 260-267 (2012).</li><li>Sheng, M., Hoogenraad, C. 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The authors declare that they have no competing financial interests.

This article presents a method for the construction of 3D meshes for modeling reaction-diffusion synaptic plasticity processes in a dendritic segment with dendritic spines. The developed model contains a dendritic segment with few dendritic spines. The lateral diffusion and reaction of AMPARs with synaptic anchors allow the simulation of the basal dynamics. The critical steps in the protocol are cutting the sphere for the creation of the top of the spine head (Figure 1, ...

Synaptic plasticity has been associated with learning and memory1. Synaptic plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), is associated respectively with the insertion and removal of AMPA receptors (AMPARs) in and out of the synaptic membrane2. The AMPAR synapses are located on top of the small volume structures called dendritic spines3. Each spine contains a protein dense region in the postsynaptic membrane called the postsynaptic density (PSD). Anchor proteins at the PSD trap AMPARs in the synaptic region. There are few copies of AMPARs within a single synapse and the trafficking and reaction of AMPARs with other species in dendritic spines is a stochastic process2,4. There are several compartmental models of synaptic receptor trafficking at dendritic spines5,6,7,8. However, there is a lack of stochastic computational models of the trafficking of AMPARs associated with synaptic plasticity at the 3D structures of the dendrites and their dendritic spines.
Computational modeling is a useful tool to investigate the mechanisms underlying the dynamics of complex systems such as the reaction-diffusion of AMPARs in dendritic spines during the occurrence of synaptic plasticity9,10,11,12. The model can be used to visualize complex scenarios, varying sensitive parameters and making important predictions in scientific conditions involving many variables that are difficult or impossible to control experimental12,13. Defining the level of detail of a computational model is a fundamental step in obtaining accurate information about the modeled phenomenon. An ideal computational model is a delicate balance between complexity and simplicity to capture the essential characteristics of the natural phenomena without being computationally prohibitive. Computational models that are too detailed can be expensive to compute. On the other hand, systems that are poorly detailed can lack the fundamental components that are essential to capture the dynamics of the phenomenon. Although 3D modeling of dendritic spines is computationally more expensive than 2D and 1D, there are conditions, such as in complex systems with many nonlinear variables reacting and diffusing in time and 3D space, for which modeling at a 3D level is essential to obtain insights about the functioning of the system. Further, the complexity can be reduced carefully to preserve the essential characteristics of a lower-dimensional model.
In a stochastic system with few copies of a given species within a small volume, the average dynamics of the system deviates from the average dynamics of a large population. In this case, the stochastic computational modeling of reaction-diffusing particles is required. This work introduces a method for stochastic modeling reaction-diffusion of a few copies of AMPARs in 3D dendritic spines. The purpose of this method is to develop a 3D computational model of a dendritic segment with dendritic spines and their synapses for modeling synaptic plasticity.
The method uses the software&#160;MCell to solve the model numerically, Blender for constructing 3D meshes, and CellBlender to create and visualize the MCell simulations, including the spatiotemporal reaction-diffusion of molecules in 3D meshes14,15,16. Blender is a suite for the creation of meshes and CellBlender is an add-on for the base software Blender. MCell is a Monte Carlo simulator for the reaction-diffusion of single molecules17.
The rationale behind the use of this method consists of modeling synaptic plasticity to achieve a better understanding of this phenomenon in the microphysiological environment of the dendritic spines14. Particularly, this method allows the simulation of homosynaptic potentiation, homosynaptic depression, and heterosynaptic plasticity between dendritic spines14.
The features of this method include modeling the 3D geometric structure of the dendrite and its synapses, the diffusion by random walk, and the chemical reactions of the molecules involved with synaptic plasticity. This method provides the advantage of creating rich environments to test hypotheses and make predictions about the functioning of a complex nonlinear system with a large number of variables. In addition, this method can be applied not only for studying synaptic plasticity but also for studying stochastic reaction-diffusion of molecules in 3D mesh structures in general.
Alternatively, 3D meshes of dendritic structures can be constructed directly in Blender from electron microscope serial reconstructions18. Although meshes based on serial reconstructions provide 3D structures, access to the experimental data is not always available. Thus, the construction of meshes adapted from basic geometric structures, as described in the present protocol, provides flexibility to develop customized dendritic segments with dendritic spines.
Another alternative computational method is the bulk simulation of well-mixed reactions in a regular volume9,10,11,19,20,21,22. The bulk simulations are very efficient in solving the reactions of many species within a single well-mixed volume23, but the bulk approach is extremely slow to solve the reaction-diffusion of molecules within many well-mixed voxels in a high-resolution 3D mesh. On the other hand, the present method using MCell simulations of reaction-diffusion of individual particles works efficiently in high-resolution 3D meshes15.
Before using this method, one should ask whether the phenomenon studied requires a stochastic reaction-diffusion approach in a 3D mesh. If the phenomenon has few copies (less than 1,000) of at least one of the reacting species diffusing in a complex geometric structure with small volume compartments such as dendritic spines, then stochastic modeling of reaction-diffusion in 3D meshes is appropriate for the application.
There are several steps required to construct a 3D computational model of a dendritic segment containing dendritic spines with synaptic plasticity. The main steps are the installation of the proper software for the construction of the model, the construction of a single dendritic spine to be used as a template to create multiple spines, and the creation of a dendritic segment that is connected with multiple dendritic spines. The step for modeling synaptic plasticity consists of inserting anchors at&#160;the PSD region and AMPARs in the dendritic segment and dendritic spines. Then, kinetic reactions between the anchors located at&#160;the PSD and AMPARs are defined to produce complexed anchor-AMPAR species that trap the AMPARs at&#160;the synaptic region. Respectively, the increase and decrease of the affinity between the anchors and the synaptic AMPARs create the process of LTP and LTD.

<table>
	<tbody>
		<tr>
			<td>Blender</td>
			<td>Blender Foundation</td>
			<td></td>
			<td>https://www.blender.org/</td>
		</tr>
		<tr>
			<td>CellBlender</td>
			<td>University of Pittsburgh</td>
			<td></td>
			<td>https://mcell.org/</td>
		</tr>
		<tr>
			<td>Mcell</td>
			<td>University of Pittsburgh</td>
			<td></td>
			<td>https://mcell.org/</td>
		</tr>
	</tbody>
</table>

3d modeling of dendritic spines with synaptic plasticity

Computational modeling of diffusion and reaction of chemical species in a three-dimensional (3D) geometry is a fundamental method to understand the mechanisms of synaptic plasticity in dendritic spines. In this protocol, the detailed 3D structure of the dendrites and dendritic spines is modeled with meshes on the software Blender with CellBlender. The synaptic and extrasynaptic regions are defined on the mesh. Next, the synaptic receptor and synaptic anchor molecules are defined with their diffusion constants. Finally, the chemical reactions between synaptic receptors and synaptic anchors are included and the computational model is solved numerically with the software MCell. This method describes the spatiotemporal path of every single molecule in a 3D geometrical structure. Thus, it is very useful to study the trafficking of synaptic receptors in and out of the dendritic spines during the occurrence of synaptic plasticity. A limitation of this method is that the high number of molecules slows the speed of the simulations. Modeling of dendritic spines with this method allows the study of homosynaptic potentiation and depression within single spines and heterosynaptic plasticity between neighbor dendritic spines.

These results provide the steps for the construction of a 3D mesh that simulates a dendritic spine with a spine head and spine neck (Figure 1 to Figure 4). In addition, multiple dendritic spines can be inserted in a single dendritic segment (Figure 5) to study heterosynaptic plasticity of AMPARs14. The PSD on the top of the spine head (Figure 6) is the place where synaptic anchor...

Watch this Scientific Journal Video about 3D Modeling of Dendritic Spines with Synaptic Plasticity at JoVE.com

3D Modeling of Dendritic Spines with Synaptic Plasticity

The protocol develops a three-dimensional (3D) model of a dendritic segment with dendritic spines for modeling synaptic plasticity. The constructed mesh can be used for computational modeling of AMPA receptor trafficking in the long-term synaptic plasticity using the software program Blender with CellBlender and MCell.

Computational modeling of diffusion and reaction of chemical species in a three-dimensional (3D) geometry is a fundamental method to understand the ...

Computational 3D Geometric Modeling of chemical species reaction through fusion. is a useful method for studying the mechanisms of receptor trafficking in and out of dendritic spine is doing synaptic plasticity. This technique has the advantage of creating a rich environment for making hypotheses and projections about nonlinear systems with a large number of variables.To create a mesh of a single dendritic spine with a spine head and a spine neck using a modified sphere. First open Blender. With Cell Blender already installed and press 5 on the keypad, to change from the perspective to the orthogonal view.Press 1 to change to front view and press Shift C to center the cursor. To create the spine head press Shift A to open the Mesh palette, and select the Mesh. Select UV Sphere and in the add UV Sphere, set the size to 0.25 and the rings to 32.To make the top of the head flat press Tab to switch Blender from the Object mode to the Edit mode. Press B to select the top three quarters of the sphere and press Delete, select Vertices and Enter to remove the vertices. Press B and select the top of the sphere.Press E, S, 0 and Enter to seal the top of the vertices still selected. And move the blue arrow down to align to the top of the spine head. To increase the mesh resolution at the top of the spine, select Tool and knife and use the knife to cut a circle around the center of the top.Then select Tool and Loop cut and slide four times to create four concentric circles around the center of the top. To create the spine neck press B and select the bottom of the mesh. Press Delete Vertices and B and select the bottom of the mesh.Press E and Z and select minus 0.45 to create an extrusion to the Z axis position, at minus 0.45 micro meters. To make the mesh compatible with M-cell press Ctrl T to triangulate the mesh and select Tool and Remove doubles. To create a multiple spine Dendrite press Shift A to open the Mesh palette and select Mesh and Cylinder.In the Add Cylinder menu, set the radius 0.3 micrometers and the depth to two micrometers and press Enter. Press R and enter 90 to rotate the cylinder 90 degrees and use the blue arrow to drag the cylinder to the bottom of the spine. Press 3 to obtain a front view of the cylinder and press Z to make the mesh transparent.Use the blue normal arrow to move the base of the spine to the interior of the cylinder and right click to select the Dendrite. Select Modifier and Add Modifier and select Boolean, Operation Union and select Object Spine. Click Apply to create a joint mesh of the Dendrite and the spine.Then use the mouse to select the mesh of the isolated spine, changing the position and angle to insert each new spine in a physiological position. To create AMPARs, select Molecules and to insert a new molecule. Change the name to AMPAR and change the molecule type to Surface molecule.Then change the diffusion constant 0.05 times 10 to the eighth square centimeters per second. To plot the anchors bound to the AMPARs, at the PSD1 during the basal condition, open Plot Output Settings and press to define the molecules. Then set the Molecule to Anchor_AMPAR, the Object to Dendrite and the Region to PSD1.To run a Basal Condition simulation select Run simulation. And set the iterations to 30, 000 and the time step to one times 10 to the minus three seconds. Click Export and Run and wait for the simulation to end.At the end of the simulation, select Reload Visualization Data, Play animation, Plot output settings and plot to visualize the spatial temporal results. To run the homosynaptic potentiation condition select Molecule Placement and rel_anchorLTP_psd1. Select rel_anchorLTP_psd1 and change Quantity to Release to 200.Then change Quantity to Release to zero. Select rel_anchor_psd1. Change Quantity to release to zero and run the simulation as just demonstrated.Synaptic plasticity can be roughly verified through changes in the number of species of AMPARs bound to anchors at each spine. For the exact calculation of the occurrence of synaptic plasticity. It is recommended to calculate the variation of the total numbers of anchored and free AMPARs at the synapse.AMPAR homosynaptic potentiation and depression can be verified through increases and decreases in the number of anchored AMPARs respectively, caused by changes in the affinity of AMPARs by anchors in comparison to the basal condition. For example homosynaptic long-term potentiation induction at a single spine, creates a heterosynaptic long-term depression effect at the neighboring spines. Following this procedure, the model can be expanded to investigate the process of LTP and LTD induction at Dendritic spines.This methods allows the testing of hypothesis about the functioning of complex non-linear systems with a large number of variables.

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6.6K Görüntüleme.  Federal University of ABC. Füzyon yoluyla kimyasal türlerin reaksiyonunun hesaplamalı 3D Geometrik Modellemesi. dendritik omurganın içinde ve dışında reseptör ticareti mekanizmalarının incelenmesinde kullanılan yararlı bir yöntem olup sinaptik plastisite yapıyor. Bu teknik, çok sayıda değişkene sahip doğrusal olmayan sistemler hakkında hipotezler ve projeksiyonlar yapmak için zengin bir ortam yaratma avantajına sahiptir.Değiştirilmiş bir küre kullanarak bir omurga kafası ve bir omurga...