An investigation on Monod-Haldane immune response based tumor-effector-interleukin-2 interactions with treatments

Abstract In this article, a mathematical model with time delay describing tumor immune interaction with Monod–Haldane kinetic response is proposed to reveal the dynamics of related inter-cellular phenomena. Positivity of the solutions, boundedness and uniform persistence of the system are determined to ensure the well-posedness of the system. The local stability of equilibria is studied as well as the length of the delay to preserve the stability is estimated for providing the mechanism of action to control the oscillation in tumor growth. Transcritical bifurcation using Sotomayer’s theorem and Hopf bifurcation are investigated analytically and numerically. Global stability is examined before the commencement of sustained oscillations using a suitable Lyapunov function. To observe the influence of tumor growth due to uncertainty in input parameters, Latin hypercube sampling based uncertainty analysis is performed followed by sensitivity analysis. Computer simulation results are illustrated to elucidate the change of dynamical behavior due to the change of system parameters.

[1]  E. Coddington,et al.  Theory of Ordinary Differential Equations , 1955 .

[2]  Lansun Chen,et al.  Permanence and positive periodic solution for the single-species nonautonomous delay diffusive models , 1996 .

[3]  Jun Tanimoto,et al.  Analysis of SIR epidemic model with information spreading of awareness , 2019, Chaos, Solitons & Fractals.

[4]  Magda Galach,et al.  DYNAMICS OF THE TUMOR—IMMUNE SYSTEM COMPETITION—THE EFFECT OF TIME DELAY , 2003 .

[5]  D. Kirschner,et al.  A methodology for performing global uncertainty and sensitivity analysis in systems biology. , 2008, Journal of theoretical biology.

[6]  L. Coussens,et al.  Paradoxical roles of the immune system during cancer development , 2006, Nature Reviews Cancer.

[7]  A. Radunskaya,et al.  Mixed Immunotherapy and Chemotherapy of Tumors: Modeling, Applications and Biological Interpretations , 2022 .

[8]  W. Leonard,et al.  Interleukin-2 at the crossroads of effector responses, tolerance, and immunotherapy. , 2013, Immunity.

[9]  Kwang Su Kim,et al.  Optimal Treatment Strategy for a Tumor Model under Immune Suppression , 2014, Comput. Math. Methods Medicine.

[10]  D. Earn,et al.  Interactions Between the Immune System and Cancer: A Brief Review of Non-spatial Mathematical Models , 2011, Bulletin of mathematical biology.

[11]  Sandip Banerjee,et al.  Stability and bifurcation analysis of delay induced tumor immune interaction model , 2014, Appl. Math. Comput..

[12]  Chuncheng Wang,et al.  Sensitivity and uncertainty analysis of a simplified Kirschner-Panetta model for immunotherapy of tumor-immune interaction , 2015, Advances in Difference Equations.

[13]  P. Hahnfeldt,et al.  Immunoediting: evidence of the multifaceted role of the immune system in self-metastatic tumor growth , 2012, Theoretical Biology and Medical Modelling.

[14]  D. Noonan,et al.  Tumor inflammatory angiogenesis and its chemoprevention. , 2005, Cancer research.

[15]  Franz Kappel,et al.  Time delay in physiological systems: analyzing and modeling its impact. , 2011, Mathematical biosciences.

[16]  Dibakar Ghosh,et al.  The influence of time delay in a chaotic cancer model. , 2018, Chaos.

[17]  Kathleen P Wilkie,et al.  A review of mathematical models of cancer-immune interactions in the context of tumor dormancy. , 2013, Advances in experimental medicine and biology.

[18]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[19]  Davud Asemani,et al.  A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications. , 2018, Mathematical biosciences.

[20]  D. Kirschner,et al.  Modeling immunotherapy of the tumor – immune interaction , 1998, Journal of mathematical biology.

[21]  G. Nicolson,et al.  Direct in vitro lysis of metastatic tumor cells by cytokine-activated murine vascular endothelial cells. , 1991, Cancer research.

[22]  W. Sokol,et al.  Kinetics of phenol oxidation by washed cells , 1981 .

[23]  Parthasakha Das,et al.  Delayed Feedback Controller based Finite Time Synchronization of Discontinuous Neural Networks with Mixed Time-Varying Delays , 2018, Neural Processing Letters.

[24]  Jun Tanimoto,et al.  Vaccination strategies in a two-layer SIR/V-UA epidemic model with costly information and buzz effect , 2019, Commun. Nonlinear Sci. Numer. Simul..

[25]  E. H. Pryde,et al.  Transesterification kinetics of soybean oil 1 , 1986 .

[26]  I. Cohen,et al.  Modeling the influence of TH1- and TH2-type cells in autoimmune diseases. , 2000, Journal of autoimmunity.

[27]  A. Perelson,et al.  Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. , 1994, Bulletin of mathematical biology.

[28]  M. Piotrowska,et al.  Mathematical modelling of immune reaction against gliomas: Sensitivity analysis and influence of delays , 2013 .

[29]  P. Hahnfeldt,et al.  Integrated Systems and Technologies : Mathematical Oncology Tumor – Immune Dynamics Regulated in the Microenvironment Inform the Transient Nature of Immune-Induced Tumor Dormancy , 2013 .

[30]  J. Tanimoto,et al.  Dynamical behaviors for vaccination can suppress infectious disease – A game theoretical approach , 2019, Chaos, Solitons & Fractals.

[31]  M. Karin,et al.  Immunity, Inflammation, and Cancer , 2010, Cell.

[32]  Sandip Banerjee,et al.  Immunotherapy with Interleukin-2: A Study Based on Mathematical Modeling , 2008, Int. J. Appl. Math. Comput. Sci..

[33]  E. F. Armstrong Enzymes. By J.B.S. Haldane, M.A. Monographs on Biochemistry. Edited by R.H.A. Plimmer, D.Sc., and Sir F. G. Hopkins, M.A., M.B., D.Sc., F.R.S. Pp. vii+235. London: Longmans, Green & Co., 1930. Price 14s , 1930 .

[34]  Raluca Eftimie,et al.  Mathematical Models for Immunology: Current State of the Art and Future Research Directions , 2016, Bulletin of mathematical biology.

[35]  Jun Tanimoto,et al.  To vaccinate or not to vaccinate: A comprehensive study of vaccination-subsidizing policies with multi-agent simulations and mean-field modeling. , 2019, Journal of theoretical biology.

[36]  R. Weinberg,et al.  The Biology of Cancer , 2006 .