Determining the expected variability of immune responses using the cyton model

Area of Science:

• Immunology
• Mathematical Biology
• Computational Biology

Background:

• Lymphocytes proliferate and die during adaptive immune responses.
• The cyton model describes lymphocyte proliferation and survival regulation.
• Cellular fates can be highly variable, drawn from skewed distributions like lognormal.

Purpose of the Study:

• To calculate higher moments of the cyton model for expected variability.
• To develop a new analytic framework for the cyton model.
• To predict variability in immune response to mitogenic signals.

Main Methods:

• Generalized the Bellman-Harris branching process to create a new analytic framework.
• Developed two distinct approaches for predicting variability.
• Employed numerical schemes for distributions lacking analytic solutions.

Main Results:

• The new framework enables explicit calculations for certain distributions.
• Numerical schemes accurately predicted experimental lymphocyte population sizes.
• Model predictions showed remarkable accuracy for both in vivo and in vitro data.

Conclusions:

• Immune response is robust and predictable.
• Limited variation exists around the expected population size.
• Predictability holds regardless of cell division numbers or population size.