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Adoptive dendritic cell (DC)-based immunotherapy represents a promising approach to overcome peripheral tolerance against autologous tumor antigens and to maintain protective antitumor immunity. The translation of successful preclinical studies, however, appears to be hampered by new complexities associated with the clinical situation. Mathematical modeling provides the means for qualitative and quantitative analysis, predictions for complex dynamic systems in immunology, and for the design and improvement of therapeutic approaches. We present here a workable computational methodology for developing meaningful data- and hypothesis-driven mathematical models for DC-based immunotherapy with a particular focus on numerical parameter estimation and sensitivity analysis.