Antonio Vicino (Università di Siena)
A Branching Process Model for Adaptive Immune Response
Quantifying T-cell proliferation provides useful information for understanding essential features of the immune response to vaccine or infection stimulus. Mathematical models, which play an important role for this analysis, have been used almost exclusively for studying in vitro experiments.
In this contribution, we adopt a multi-type branching process to model T-cell proliferation in in vivo experiments. Since the real system consists of a complex network of connected nodes where cells circulate and proliferate, both trafficking and proliferation phenomena need be modeled.
A quasi maximum likelihood approach is adopted to estimate model parameters, using T-cell relative frequencies instead of cell counts. Parameter estimates which represent the probabilities of division, death, migration and splitting of the different cell generations, provide meaningful information on T-cell population kinetics.