11,155 research outputs found
Activation of effector immune cells promotes tumor stochastic extinction: A homotopy analysis approach
In this article we provide homotopy solutions of a cancer nonlinear model
describing the dynamics of tumor cells in interaction with healthy and effector
immune cells. We apply a semi-analytic technique for solving strongly nonlinear
systems - the Step Homotopy Analysis Method (SHAM). This algorithm, based on a
modification of the standard homotopy analysis method (HAM), allows to obtain a
one-parameter family of explicit series solutions. By using the homotopy
solutions, we first investigate the dynamical effect of the activation of the
effector immune cells in the deterministic dynamics, showing that an increased
activation makes the system to enter into chaotic dynamics via a
period-doubling bifurcation scenario. Then, by adding demographic stochasticity
into the homotopy solutions, we show, as a difference from the deterministic
dynamics, that an increased activation of the immune cells facilitates cancer
clearance involving tumor cells extinction and healthy cells persistence. Our
results highlight the importance of therapies activating the effector immune
cells at early stages of cancer progression
On the foundations of cancer modelling: selected topics, speculations, & perspectives
This paper presents a critical review of selected topics related to the modelling of cancer onset, evolution and growth, with the aim of illustrating, to a wide applied mathematical readership, some of the novel mathematical problems in the field. This review attempts to capture, from the appropriate literature, the main issues involved in the modelling of phenomena related to cancer dynamics at all scales which characterise this highly complex system: from the molecular scale up to that of tissue. The last part of the paper discusses the challenge of developing a mathematical biological theory of tumour onset and evolution
On a mathematical model of immune competition
AbstractThis work deals with the qualitative analysis of a nonlinear integro-differential model of immune competition with special attention to the dynamics of tumor cells contrasted by the immune system. The analysis gives evidence of how initial conditions and parameters influence the asymptotic behavior of the solutions
A mathematical model of tumor-immune interactions
A mathematical model of the interactions between a growing tumor and the immune system is presented. The equations and parameters of the model are based on experimental and clinical results from published studies. The model includes the primary cell populations involved in effector T cell mediated tumor killing: regulatory T cells, helper T cells, and dendritic cells. A key feature is the inclusion of multiple mechanisms of immunosuppression through the main cytokines and growth factors mediating the interactions between the cell populations. Decreased access of effector cells to the tumor interior with increasing tumor size is accounted for. The model is applied to tumors with different growth rates and antigenicities to gauge the relative importance of various immunosuppressive mechanisms. The most important factors leading to tumor escape are TGF-induced immunosuppression, conversion of helper T cells into regulatory T cells, and the limitation of immune cell access to the full tumor at large tumor sizes. The results suggest that for a given tumor growth rate, there is an optimal antigenicity maximizing the response of the immune system. Further increases in antigenicity result in increased immunosuppression, and therefore a decrease in tumor killing rate
On a Mathematical Model of Immune Competition
This work deals with the qualitative analysis of a nonlinear integro-differential model of immune competition with special attention to the dynamics of tumor cells contrasted by the immune system. The analysis gives evidence of how initial conditions and parameters influence the asymptotic behavior of the solutions
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