Software Tutorial: Comparing Simple Regulation to Negative Autoregulation

Note: We are currently in the process of updating this tutorial to the latest version of MCell, CellBlender, and Blender. This tutorial works with MCell3, CellBlender 3.5.1, and Blender 2.79. Please see a previous tutorial for a link to download these versions.

Implementing simple regulation in CellBlender

In this tutorial, we will compare simple against negative autoregulation using a particle-based simulation in CellBlender. We will start with simple regulation; if you followed the prologue, then load your CellBlender_Tutorial_Template.blend file; otherwise, follow the steps indicated in the prologue’s Random Walk Tutorial to produce this file. Then, save a copy of this file as NAR_comparison.blend. You may also download the completed tutorial files here.

Then go to CellBlender > Molecules and create the following molecules:

1. Click the + button.
2. Select a color (such as yellow).
3. Name the molecule Y1.
4. Select the molecule type as Surface Molecule.
5. Add a diffusion constant of 1e-6.
6. Up the scale factor to 5 (click and type “5” or use the arrows).

Repeat the above steps as needed to make sure that both of the following molecules are entered with the following parameters.

Molecule Name	Molecule Type	Diffusion Constant	Scale Factor
Y1	Surface	1e-6	5
X1	Surface	1e-6	1

Now go to CellBlender > Molecule Placement to set the following sites to release our molecules:

1. Click the + button.
2. Select or type in the molecule X1.
3. Type in the name of the Object/Region Plane.
4. Set the Quantity to Release as 300.

Finally, we set reactions. Go to CellBlender > Reactions and define the following reactions:

1. Click the + button.
2. Under reactants, type X1’ (note the apostrophe).
3. Under products, type X1’ + Y1’.
4. Set the forward rate as 2e2.

Repeat the above steps as needed to ensure the following reactions are present.

Reactants	Products	Forward Rate
X1’	X1’ + Y1’	4e2
Y1’	NULL	4e2

Go to CellBlender > Plot Output Settings to ensure that we will be able to plot the concentrations of our particles over time.

1. Click the + button.
2. Set the molecule name as Y1.
3. Ensure World is selected.
4. Ensure Java Plotter is selected.
5. Ensure One Page, Multiple Plots is selected.
6. Ensure Molecule Colors is selected.

We are ready to run our simulation! Visit CellBlender > Run Simulation and select the following options:

1. Set the number of iterations to 20000.
2. Ensure the time step is set as 1e-6.
3. Click Export & Run.

Once the simulation has run, click CellBlender > Reload Visualization Data to visualize the outcome.

You have the option of watching the animation within the Blender window by clicking the play button at the bottom of the screen.

Now return to CellBlender > Plot Output Settings and scroll to the bottom to click Plot.

You should be able to see Y reach a steady state, at which the number of particles essentially levels off subject to some noise.

Save your .blend file.

Adding negative auto-regulation to the simulation

Now that we have simulated simple regulation, we will implement negative autoregulation in CellBlender to compare how this system reaches steady state compared to the simple regulation system.

Go to CellBlender > Molecules and create the following molecules:

1. Click the + button.
2. Select a color (such as yellow).
3. Name the molecule Y2.
4. Select the molecule type as Surface Molecule.
5. Add a diffusion constant of 1e-6.
6. Up the scale factor to 5 (click and type “5” or use the arrows).

Repeat the above steps to make sure that we have all of the following molecules (X1 and Y1 are inherited from the simple regulation simulation).

Molecule Name	Molecule Type	Diffusion Constant	Scale Factor
Y1	Surface	1e-6	5
X1	Surface	1e-6	1
Y2	Surface	1e-6	5
X2	Surface	1e-6	1

Now go to CellBlender > Molecule Placement to set the following molecule release sites:

1. Click the + button.
2. Select or type in the molecule X2.
3. Type in the name of the Object/Region Plane.
4. Set the Quantity to Release as 300.

You should now have the following release sites.

Molecule Name	Object/Region	Quantity to Release
X1	Plane	300
X2	Plane	300

Next go to CellBlender > Reactions to create the following reactions:

1. Click the + button.
2. Under reactants, type X2’ (the apostrophe is important).
3. Under products, type X2’ + Y2’.
4. Set the forward rate as 2e2.

Repeat the above steps as needed to ensure that you have the following reactions.

Reactants	Products	Forward Rate
X1’	X1’ + Y1’	4e2
X2’	X2’ + Y2’	4e2
Y1’	NULL	4e2
Y2’	NULL	4e2
Y2’ + Y2’	Y2’	4e2

Go to CellBlender > Plot Output Settings to set up a plot as follows:

1. Click the + button.
2. Set the molecule name as Y2.
3. Ensure World is selected.
4. Ensure Java Plotter is selected.
5. Ensure One Page, Multiple Plots is selected.
6. Ensure Molecule Colors is selected.

You should now have both Y1 and Y2 plotted.

Molecule Name	Selected Region
Y1	World
Y2	World

We are now ready to run the simulation comparing simple regulation and negative autoregulation. To do so, go to CellBlender > Run Simulation and do the following:

1. Set the number of iterations to 20000.
2. Ensure the time step is set as 1e-6.
3. Click Export & Run.

Click CellBlender > Reload Visualization Data to visualize the result of the simulation.

You have the option of watching the animation within the Blender window by clicking the play button at the bottom of the screen.

Now return to CellBlender > Plot Output Settings and scroll to the bottom to click Plot.

A plot should appear in which the plot of Y over time assuming simple regulation is shown in red, and the plot of Y if negatively autoregulated is shown in yellow.

Save your file.

Comparing simple regulation and negative autoregulation

STOP: Now that you have run the simulation comparing simple regulation and negative autoregulation, are the plots of Y for the two simulations what you would expect? Why or why not?

If you find the outcome of the simulation in this tutorial confusion, don’t be concerned. In the main text, we will interpret this outcome and see if it allows us to start making conclusions about why negative autoregulation has evolved, or if we will need to further tweak our model.

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