Computational Modeling of Germinal Center Response using CC3D

Derek Mu

Montgomery Blair High School,

Silver Spring, MD

USA

All workshop sessions will be live-streamed, recorded and distributed on YouTube

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Support: NIH NIBIB-U24EB028887, NIGMS-R01GM122424, NSF-2120200, NSF-2000281, NSF-1720625, NIGMS-R01GM076692, NIGMS-R01GM077138, NIEHS Superfund P42ES04911

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Learning Objectives

• Biological ideas behind the Germinal Center Reaction
• Review of CC3D implementation
• Exporting and visualizing data generated from simulation
• Practice on exporting and visualizing data

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The adaptive humoral immune response

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T-independent

T-dependent

The germinal center response (GCR)

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The germinal center response (GCR) – a remarkable spatial-tempo phenomenon

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The germinal center response (GCR) – a remarkable spatial-tempo phenomenon

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Dark zone:

• Clonal expansion
• Somatic hypermutation (SHM)
• Class switch recombination (CSR)

Light zone:

• Positive selection

CXCL13

CXCL12

FDC

(CXCR5)

(CXCR4)

Motivation and Hypothesis

• Many environmental chemicals can disrupt GCR by perturbing the molecular network driving B cell response.

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• We hypothesize that having a mathematical model integrating the molecular network and the spatial behaviors of B cells in GC can help us to better understand and predict the health risk of environmental immunotoxicants.

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Modeling the B cell intracellular network in GCR using Tellurium

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CXC13

CXC12

Simplification

Implement in Tellurium

Key Biological Events Simulated

1. Major participating cell types
1. B cells
2. Follicular dendritic cells (FDCs) in the Light zone
3. Cxcl12-expressing reticular cells (CRCs) in the Dark zone
4. T follicular helper (Tfh) cells in the Light zone
2. B cell chemotaxis
• Dictated by chemoattractant gradient and expressed receptors
3. Light zone B cell interactions
• With FDCs: Antigen Capture
• With Tfh cells: Positive Selection
4. Dark zone B cell interactions
• B Cell proliferation and somatic hypermutation

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Simulated B cell dynamics (color denote antibody affinity)

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Files Used in Simulation Program

1. Main Python Script
2. XML Script
3. Python
4. PIF File
5. Python script for exporting & processing data

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Construction of the Computational Model

The mathematical model of GCR was implemented in the CompuCell3D (CC3D) platform.

A 100x100 2D plane was simulated.

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PIF File

A PIF file was used in order to create the�surrounding wall:

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Seeding Cells

CRC were randomly generated in the dark zone by picking random values within an ellipse.

FDC and T cells were randomly generated in the light zone using the same method.

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Establishing B Cell Clones

• The target sequence was initialized as “AGTCT”, representing the sequence that most effectively neutralizes the antigen presented by FDCs.
• Random five digit sequences (5) were chosen and stored into a list to be later assigned to B Cells.

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Generating B cells (1)

New B cells were randomly generated and assigned a sequence from the list containing randomly generated sequences; this process was repeated until no more sequences remained.

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Seeding Cells

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CRC

B cell

FDC

Tfh cell

Assign Antigen to FDC

All FDCs were assigned a predetermined target sequence as the antigen presenting cells.

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Generating B cells (2)

Newly generated B cells were assigned a set of parameters (volume, chemotaxis data, Tellurium gene network model):

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Chemotaxis

In order to simulate chemotaxis, the �chemotaxis plugin and DiffusionSolverFE �were initialized in the XML file:

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Establishing Chemoattractant Gradients

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CXCL13 field

CXCL12 field

CRC

B cell

FDC

Tfh cell

Tellurium Model

• Tellurium was used to simulate the intracellular molecular network.�
• The migration and mitosis behavior of B cells is driven by a gene network:

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B Cell interactions with FDC, T Cells, and CRC

Using the neighbor tracker plugin, it was determined whether or not the B Cell is in contact with an FDC, CRC, or T Cell.

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B Cell interaction with Follicular Dendritic Cells (FDCs)

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B Cell interaction with T follicular helper cells (Tfh cells)

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Cell Death Timer

The drop of NF-kB level to a certain threshold was used to initiate a death timer which follows a stochastic first order process.

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B Cell Growth

B Cell growth is initiated by AP4, which is activated by MYC, which is activated by NF-kB.

After B Cell touches CRC, if the AP4 level is still greater than 10, B Cell growth is initiated by increasing cell volume following an exponential function.

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B Cell Division (Mitosis)

In MitosisSteppable class, if the volumes of B Cells reaches 32 or higher, the cells undergo mitosis and divide.

The parent cell’s dictionary attributes (affinityScore, perfectScore, xCOM, yCOM, gene network parameters) are then duplicated to the two daughter cells, and the parent’s protein levels are divided by two.

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B Cell Somatic Hypermutation

If the B Cell’s affinity score is less than the max possible score, the cell undergoes mutation, where a random element of the sequence is selected and randomly changed to a different base.

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Simulated B cell dynamics (color denote antibody affinity)

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Simulation Output

• Values for each B cell are stored in unique output .txt file.
• The files are named as a_b_c.txt, where a is the cell’s generation, b is the ID of the mother cell, and c is the ID of the cell.
• Data stored in a list in each B cell’s dictionary is appended to output .txt files at a set intervals dictated by a save frequency.

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Visualization of Cell Lineage

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Plotting Model Outputs Using .txt Files

1. .txt files from CC3D
2. Import into python
3. Recursion to find lineages
4. Recursion to combine

desired lineage

1. Select desired variables
2. Graph using matplotlib

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a_b_c.txt files from CC3D

a: Cell generation

b: Mother ID

c: Cell ID

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a_b_c.txt files from CC3D

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Import into python

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Recursion to find lineages

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Recursion to find lineages

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Recursion to combine desired lineage

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Select desired variables to display and analyze

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Graph using matplotlib

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Trajectory of a single B cell and subsequent daughter cells and CXCR4 expression levels

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Spatial-temporal dependent gene expression and cell generation

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Evolution of antibody affinity and B cell number

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mcs=0

mcs=45000

Inhibitory Effect of dioxin on cell proliferation and affinity maturation

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mcs=0

mcs=45000

Dioxin

AhR

Summary

• The preliminary CC3D model qualitatively recapitulates the key morphological and functional events of GCR.

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• Future iteration is needed to improve the virtual GCR model to better understand and predict the adverse outcomes of immune suppressing chemicals.

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Exercises: Plotting Model Outputs Using .txt Files

1) Select variables (3) to plot

Make sure capitalization is correct!

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Exercises: Plotting Model Outputs Using .txt Files

2) Changing cell lineage

Copy a cell lineage from the cellLineages.txt file and replace the current lineage (line 37)

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Exercises: Plotting Model Outputs Using .txt Files

3) Formatting output

Changing output from 2D plot to 3D:

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Acknowledgments

Gangarosa Department of Environmental Health

Rollins School of Public Health

Emory University

Atlanta, GA

Qiang Zhang, M.D., Ph.D.

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Funding: NIEHS Superfund P42ES04911

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