Mathematical modeling reveals the biological program regulating lymphopenia-induced proliferation.

Area of Science:

• Immunology
• Computational Biology
• Cell Biology

Background:

• T cell activation via T cell receptor (TCR) binding to peptide-MHC complexes drives T lymphocyte proliferation.
• While foreign peptide recognition triggers division and differentiation, self-peptide recognition in lymphopenic conditions induces slower divisions, with or without differentiation.
• It remains unclear if these distinct T cell responses stem from separate cell cycle control programs.

Purpose of the Study:

• To investigate the distinct cell cycle control mechanisms governing T cell proliferation in lymphopenic conditions.
• To differentiate between deterministic and stochastic models of T cell division using mathematical modeling.

Main Methods:

• Utilized a mathematical modeling approach to analyze the proliferative response of TCR transgenic F5 T cells.
• Compared two models: an
• autopilot
• deterministic burst model and a single stochastic division model.
• Validated model predictions experimentally regarding division onset, rate, and nature.

Main Results:

• The
• autopilot
• deterministic model poorly described T cell responses to lymphopenia.
• The single stochastic division model closely fitted experimental data for T cell proliferation in lymphopenia.
• Experimental validation confirmed the model's predictions on T cell division dynamics.

Conclusions:

• T cell division induced by lymphopenia is best described by a process of single stochastic divisions.
• This stochastic division mechanism appears optimized for T cell homeostasis rather than differentiation.
• The findings suggest distinct regulatory pathways for T cell proliferation in response to foreign antigens versus lymphopenic self-peptide stimulation.