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The immune system turns out to have both stimulatory and inhibitory factors influencing on tumor growth. In recent years, the pro-tumor role of immunity factors such as regulatory T cells and TGF-β cytokines has specially been considered in mathematical modeling of tumor-immune interactions. This paper presents a novel structural methodology for reviewing these models and classifies them into five subgroups on the basis of immune factors included. By using our experimental data due to immunotherapy experimentation in mice, these five modeling groups are evaluated and scored. The results show that a model with a small number of variables and coefficients performs efficiently in predicting the tumor-immune system interactions. Though immunology theorems suggest to employ a larger number of variables and coefficients, more complicated models are here shown to be inefficient due to redundant parallel pathways. So, these models are trapped in local minima and restricted in prediction capability. This paper investigates the mathematical models that were previously developed and proposes variables and pathways that are essential for modeling tumor-immune. Using these variables and pathways, a minimal structure for modeling tumor-immune interactions is proposed for future studies. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm inspired on the T-Cell model of the immune system (i.e., an artificial immune system), which is used to solve economic dispatch problems. The proposed approach is called IA_EDP, which stands for Immune Algorithm for Economic Dispatch Problem , and it uses two versions of a redistribution power operator which tries to keep feasible the solutions that it finds. The proposed approach is validated using eight problems taken from the specialized literature. Our results are compared with respect to those obtained by several other approaches. We also perform some statistical analysis in order to determine the sensitivity of our proposed approach to its parameters. [ABSTRACT FROM AUTHOR]

• Obtain threshold values and formulas of the Hopf bifurcation. • Show the interesting chaotic attractors and the periodic oscillation. • Use bifurcation theory to explain the chaotic attractor formation. • Obtain the biomedical significance corresponding to model dynamics. In this paper, a tumor and Lymphatic immune system interaction model with two time delays is discussed in which the delays describe the proliferation of tumor cells and the transmission from immature T lymphocytes to mature T lymphocytes respectively. Conditions for the asymptotic stability of the equilibrium and the existence of Hopf bifurcations are obtained by analyzing the roots of a characteristic equation. The computing formulas for the stability and the direction of the Hopf bifurcating periodic solutions are given. Numerical simulation show that different values of time delays can generate different behaviors, including the stable-state, the periodic oscillation and the chaotic attractors, as well as the coexistence of two periodic oscillations. These theoretical and numerical results not only can be useful for explaining the occurrence of chaotic attractors, but also can help for understanding the biomedical significance corresponding to the interaction dynamics of tumor cells and T lymphocytes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a metaheuristic inspired on the T-Cell model of the immune system (i.e., an artificial immune system) is introduced. The proposed approach (called DTC, for Dynamic T-Cell) is used to solve dynamic optimization problems, and is validated using test problems taken from the specialized literature on dynamic optimization. Results are compared with respect to artificial immune approaches representative of the state-of-the-art in the area. Some statistical analyses are also performed, in order to determine the sensitivity of the proposed approach to its parameters. [Copyright &y& Elsevier]

Abstract: This paper investigates a viral infection model with periodic immune response and nonlinear cytotoxic T lymphocyte (CTL) response. Using the periodic rhythms of human immune system, the model can avoid the unreasonable equilibrium in the basic viral model with nonlinear CTL response introduced by Nowak et al. We obtain the global stability of the infection-free equilibrium and the immune-exhausted equilibrium. Numerical simulations show that the oscillation of immune system can affect the pattern of the viral dynamical behaviors. Period doubling bifurcations of the system are observed via simulations. This can provide a possible interpretation for the viral oscillation behaviors, which were observed in chronic HBV and HCV infection patients. [ABSTRACT FROM AUTHOR]

Abstract: Immunodominance is a common phenomenon observed in multiple epitopes immune systems. Previous studies hypothesize that the competition among CD8+ T cell responses against different epitopes can be used to explain immunodominance. This paper proposes a mathematical model that describes the dynamics of CD8+ T cells primed by antigen-presenting dendritic cells (DCs) in the lymph nodes, and shows that the overall avidity of the interactions between peptide-specific T cells and cognate antigen-bearing DCs may determine the immunodominance. The model suggests the probability that a peptide-specific T cell be immunodominant is proportional to (1) the cognate T cell receptor (TCR) affinity, (2) the number of complexes of cognate peptide and major histocompatibility complex (pMHC) per DC, and (3) the half-life of cognate peptide-specific pMHC. The model predicts a threshold density of pMHC complexes for T cell activation. These observations from the mathematical model are consistent with experimental studies in the open literature. For DC-based vaccine design, the model suggests a strategy of immunotherapy based on the injection of cognate antigen-pulsed DCs. [Copyright &y& Elsevier]