title: Harnessing the heterogeneity of T cell differentiation fate to fine-tune generation of effector and memory T cells.
abstract: Recent studies show that naïve T cells bearing identical T cell receptors experience heterogeneous differentiation and clonal expansion processes. The factors controlling this outcome are not well characterized, and their contributions to immune cell dynamics are similarly poorly understood. In this study, we develop a computational model to elaborate mechanisms occurring within and between two important physiological compartments, lymph nodes and blood, to determine how immune cell dynamics are controlled. Our multi-organ (multi-compartment) model integrates cellular and tissue level events and allows us to examine the heterogeneous differentiation of individual precursor cognate naïve T cells to generate both effector and memory T lymphocytes. Using this model, we simulate a hypothetical immune response and reproduce both primary and recall responses to infection. Increased numbers of antigen-bearing dendritic cells (DCs) are predicted to raise production of both effector and memory T cells, and distinct "sweet spots" of peptide-MHC levels on those DCs exist that favor CD4+ or CD8+ T cell differentiation toward either effector or memory cell phenotypes. This has important implications for vaccine development and immunotherapy.
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI , USA.
authors.forename: Chang
authors.initials: C
authors.lastname: Gong
authors.affiliation: Department of Chemical Engineering, University of Michigan , Ann Arbor, MI , USA.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School , Ann Arbor, MI , USA.
authors.forename: Denise
authors.initials: D
authors.lastname: Kirschner
keywords: agent-based; blood; circulation; cognate; dendritic cells; lymph nodes; systems biology; two-compartment model