Influence of distributed delays on the dynamics of a generalized immune system cancerous cells interactions model

•An immune system-tumour interactions model with distributed time delay is proposed.•Mathematical properties of the model and its dynamic are analytically studied.•Different Erlang and piecewise linear delay distributions are considered.•Model with each distributed delay is validated with sets of ex...

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Bibliographic Details
Published in:Communications in nonlinear science & numerical simulation 2018-01, Vol.54, p.389-415
Main Authors: Piotrowska, M.J., Bodnar, M.
Format: Article
Language:English
Subjects:
Cells
Computer simulation
Differential equations
Distributed delays
Estimation of parameters
Hopf bifurcation
Mathematical models
Ordinary differential equations
Parameter estimation
Probability distribution
Stability
Stability analysis
Steady state
Tumors
Tumour-immune system interactions
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Summary:•An immune system-tumour interactions model with distributed time delay is proposed.•Mathematical properties of the model and its dynamic are analytically studied.•Different Erlang and piecewise linear delay distributions are considered.•Model with each distributed delay is validated with sets of experimental data.•The biological conclusions indicating a possibility of tumour control are formulated. We present a generalisation of the mathematical models describing the interactions between the immune system and tumour cells which takes into account distributed time delays. For the analytical study we do not assume any particular form of the stimulus function describing the immune system reaction to presence of tumour cells but we only postulate its general properties. We analyse basic mathematical properties of the considered model such as existence and uniqueness of the solutions. Next, we discuss the existence of the stationary solutions and analytically investigate their stability depending on the forms of considered probability densities that is: Erlang, triangular and uniform probability densities separated or not from zero. Particular instability results are obtained for a general type of probability densities. Our results are compared with those for the model with discrete delays know from the literature. In addition, for each considered type of probability density, the model is fitted to the experimental data for the mice B-cell lymphoma showing mean square errors at the same comparable level. For estimated sets of parameters we discuss possibility of stabilisation of the tumour dormant steady state. Instability of this steady state results in uncontrolled tumour growth. In order to perform numerical simulation, following the idea of linear chain trick, we derive numerical procedures that allow us to solve systems with considered probability densities using standard algorithm for ordinary differential equations or differential equations with discrete delays.
ISSN:1007-5704
1878-7274
DOI:10.1016/j.cnsns.2017.06.003