A multiscale spatial modeling framework for the germinal center response.

Area of Science:

• Immunology
• Computational Biology
• Systems Biology

Background:

• The germinal center reaction (GCR) is crucial for adaptive humoral immunity, generating high-affinity antibodies and memory B cells.
• Understanding the complex molecular and cellular dynamics within germinal centers (GCs) is essential for insights into immune responses.
• Existing models often lack multiscale integration, limiting comprehensive analysis of GC processes.

Purpose of the Study:

• To develop a novel, multiscale spatial simulation framework for the germinal center reaction.
• To integrate molecular signaling networks with cellular behaviors and tissue-level dynamics within a virtual GC.
• To provide an open-source platform for investigating adaptive humoral immunity.

Main Methods:

• Developed a hybrid stochastic GC framework using the Cellular Potts Model (CPM) on the CompuCell3D platform.
• Employed Tellurium to simulate intracellular molecular networks (NF-κB, FOXO1, MYC, AP4, CXCR4, BLIMP1) responding to B cell receptor (BCR) and CD40 signaling.
• Linked molecular network outputs to B cell spatiotemporal behaviors, including migration, proliferation, mutation, apoptosis, and selection.

Main Results:

• The simulation successfully recapitulated key GC events: B cell population growth, affinity maturation, and clonal dominance.
• Modeled B cell cyclic migration between dark and light zones, proliferation, somatic hypermutation, and selection processes.
• Demonstrated the framework's ability to simulate molecular, cellular, and morphological aspects of the GCR.

Conclusions:

• The developed framework offers a powerful, open-source, and customizable virtual platform for in silico GC research.
• This multiscale approach enables deeper understanding of mechanistic and applied questions in adaptive humoral immunity.
• Facilitates future qualitative and quantitative investigations into immune response dynamics.