Overview
This project develops a hierarchy of mathematical models—ranging from ordinary differential equations and stochastic agent-based simulations to partial differential equations—designed to predict how immune cells sense and integrate environmental and metabolic signals. By coupling extracellular ligand fields with intracellular signaling cascades (e.g., TLR→MYD88→NF-κB and PI3K-AKT-mTOR pathways), the framework captures:
- Single-cell fate decisions – how distinct cytokine and metabolite combinations bias naïve dendritic cells toward immunogenic or tolerogenic phenotypes.
- Population-level dynamics – how clonal expansion, death, and replenishment shape cell-type composition within tissues and lymphoid organs.
- Spatial migration and encounter statistics – how DCs migrate along chemokine gradients, enter lymph-node paracortex, and form productive contacts with CD4⁺ and CD8⁺ T cells.
- Homeostatic balance – how feedback between bone-marrow progenitor output and peripheral demand maintains steady-state cell numbers.
Together, these multi-scale models could illuminate, for example, how dendritic cells orchestrate adaptive immunity: capturing antigen in peripheral tissues, traversing lymphatics, presenting peptide–MHC complexes, and delivering Signal-2 co-stimulation to T cells—all while preserving systemic homeostasis.
By unifying diverse biological data into a coherent computational framework, this work provides both a conceptual map and a predictive toolkit for immunologists seeking to understand and manipulate immune-cell behavior in health and disease.