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2020 | OriginalPaper | Chapter

Stability Investigation of Biosensor Model Based on Finite Lattice Difference Equations

Authors : Vasyl Martsenyuk, Aleksandra Klos-Witkowska, Andriy Sverstiuk

Published in: Difference Equations and Discrete Dynamical Systems with Applications

Publisher: Springer International Publishing

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Abstract

We consider the delayed antibody-antigen competition model for two-dimensional array of biopixels
$$\begin{aligned} \begin{aligned} x_{i,j}(n+1)&=x_{i,j}(n)\exp \big \{\beta - \gamma y_{i,j}(n-r) - \delta _x x_{i,j}(n-r) \big \} + \hat{S}\left\{ x_{i,j}(n) \right\} ,\\ y_{i,j}(n+1)&=y_{i,j}(n)\exp \big \{-\mu _y + \eta \gamma x_{i,j}(n-r) - \delta _y y_{i,j}(n) \big \}, i,j=\overline{1,N}, \end{aligned} \end{aligned}$$
\(n, r\in \mathbb {N}\). Here \(x_{i,j}(t)\) is the concentration of antigens, \(y_{i,j}(t)\) is the concentration of antibodies in biopixel (ij), \(i,j=\overline{1,N}\). \(\hat{S} \{ x_{i,j}(n)\} = (D/\varDelta ^2)\{x_{i-1,j}(n)+x_{i+1,j} (n)+x_{i,j-1}(n)+x_{i,j+1}(n) - 4x_{i,j}(n)\}\) is spatial diffusion-like operator. Permanence of the system is investigated. Stability research uses approach of Lyapunov functions. Numerical simulations are used in order to investigate qualitative behavior when changing the value of time delay \(r \in \mathbb {N}\) and diffusion \(D/\varDelta ^2\). It was shown that when increasing the value of time delay r, we transit from steady state through Hopf bifurcation, increasing period and finally to chaotic behavior. The increase of diffusion causes an appearance of chaotic solutions also.

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Footnotes
1
Diffusion term is considered be additive in order to get clear permanence and stability results. Actually the diffusion on discrete space may be represented by a matrix multiplication also [4].
 
2
Lemma 4 offers necessary condition (12).
 
3
In order to substantiate it, we assume the contrary, namely, there are \(\epsilon _1>0\) and \(i^\star ,j^\star \in \overline{1,N}\) such that \(x_{i^\star ,j^\star }(n)>M_{x,i^\star ,j^\star }(r)\frac{\exp (\beta - 1)}{\delta _x} + \epsilon _1\), for all \(n>0\). Then
$$\begin{aligned} \begin{aligned} \lim _{n\rightarrow \infty } \sup \sum _{i,j=1}^N{x_{i,j}(n)}&\le \frac{\exp (\beta -1)}{\delta _x}\sum _{i,j=1}^N{M_{x,i,j}(r)} < \frac{\exp (\beta -1)}{\delta _x}\sum _{i,j=1,i\ne i^\star , j\ne j^\star }^N{M_{x,i,j}(r)}\\ + x_{i^\star ,j^\star } - \epsilon _1&\le \frac{\exp (\beta -1)}{\delta _x}\sum _{i,j=1}^N{M_{x,i,j}(r)} + \hat{S}\left\{ x_{i^\star ,j^\star }(n-1) \right\} - \epsilon _1, \end{aligned} \end{aligned}$$
which is a contradiction at \(n\rightarrow \infty \).
 
4
Here we use that \(\frac{1}{x} \exp (x-1)>1\) for \(x>0\).
 
5
Here we use epidemiological term “endemic” meaning the state when the “infection” (in this context, antigen) is constantly maintained at a baseline level in an area without external inputs.
 
6
Hereinafter we omit units of dimensions of parameters.
 
7
After scaling of \(\gamma \) the value \(\eta = 0.8/\gamma \) may not be applicable for Nicholson-type difference system (it causes number overflow). So, we have decreased it to 0.01184 experimentally.
 
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Metadata
Title
Stability Investigation of Biosensor Model Based on Finite Lattice Difference Equations
Authors
Vasyl Martsenyuk
Aleksandra Klos-Witkowska
Andriy Sverstiuk
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-35502-9_13

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