Discretization of a Mathematical Model for Tumor-Immune System Interaction with Piecewise Constant Arguments

Applied Mathematics and Sciences: An International Journal (MathSJ ), Vol. 1, No. 1, May 2014
57
DISCRETIZATION OF A MATHEMATICAL MODEL FOR
TUMOR-IMMUNE SYSTEM INTERACTION WITH
PIECEWISE CONSTANT ARGUMENTS
Senol Kartal1
and Fuat Gurcan2
1
Department of Mathematics, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey
2
Department of Mathematics, Erciyes University, Kayseri, Turkey
2
Faculty of Engineering and Natural Sciences, International University of Sarajevo,
Hrasnicka cesta 15, 71000, Sarejevo, BIH
ABSTRACT
The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor
and immune cells. The model consists of differential equations with piecewise constant arguments and
based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is
obtained a system of difference equations from the system of differential equations. In order to get local
and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion
and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a
consequence of Neimark-Sacker bifurcation.
KEYWORDS
piecewise constant arguments; difference equation; stability; bifurcation
1. INTRODUCTION
In population dynamics, the simplest and most widely used model describing the competition of
two species is of the Lotka-Volterra type. In addition, there exist numerous extensions and
generalizations of this type model in tumor growth model [1-8]. In 1995, Gatenby [1] used Lotka-
Volterra competition model describing competition between tumor cells and normal cells for
space and other resources in an arbitrarily small volume of tissue within an organ. On the other
hand, Onofrio [2] has presented a general class of Lotka-Volterra competition model as
follows:
x.
= x(f(x) − ϕ(x)y),
y.
= β(x)y − μ(x)y + σq(x) + θ(t).
(1)
Here x and y denote tumor cell and effector cell sizes respectively. The function f(x) represents
tumor growth rates and there are many versions of this term. For example, in Gompertz model:
f(x) = αLog(A/x) [3], the logistic model: f(x) = α(1 − x/A) [4].
The metamodel (1) also includes following exponential model which has been constructed by
Stepanova [6].

58
x.
= μ x(t) − γx(t)y(t),
y.
= μ (x(t) − βx(t) )y(t) − δy(t) + κ,
(2)
where x and y denote tumor and T-cell densities respectively. In this model, μ is the
multiplication rate of tumors, γ is the rate of elimination of cancer cells by activity of T-cells, μ
represents the production of T-cells which are stimulated by tumor cells, β denotes the
saturation density up from which the immunological system is suppressed, δ is the natural death
rate of T cell and κ is the natural rate of influx of T cells from the primary organs [3].
Recently, it has been observed that the differential equations with piecewise constant arguments
play an important role in modeling of biological problems. By using a first-order linear
differential equation with piecewise constant arguments, Busenberg and Cooke [9] presented a
model to investigate vertically transmitted. Following this work, using the method of reduction to
discrete equations, many authors have analyzed various types of differential equations with
piecewise constant arguments [10-19]. The local and global behavior of differential equation
dx(t)
dt
= rx(t){1 − αx(t) − β x([t]) − β x([t − 1])} (3)
has been analyzed by Gurcan and Bozkurt [10]. Using the equation (3), Ozturk et al [11] have
modeled a population density of a bacteria species in a microcosm. Stability and oscillatory
characteristics of difference solutions of the equation
dx(t)
dt
= x(t) r 1 − αx(t) − β x([t]) − β x([t − 1]) + γ x([t]) + γ x([t − 1]) (4)
has been investigated in [12]. This equation has also been used for modeling an early brain tumor
growth by Bozkurt [13].
In the present paper, we have modified model (2) by adding piecewise constant arguments such
as
x.
= μ x(t) − γx(t)y([t]),
y.
= μ (x([t]) − βx([t]) )y(t) − δy(t) + κ,
(5)
where [t] denotes the integer part of t ϵ [0, ∞) and all these parameters are positive.
2. STABILITY ANALYSIS
In this section, we investigate local and global stability behavior of the system (5). The system
can be written in the interval t ϵ [n, n + 1) as
⎩
⎨
⎧
dx
x(t)
= μ − γy(n) d(t),
dy
dt
+ βμ x(n) + δ − μ x(n) y(t) = κ.
(6)
Integrating each equations of system (6) with respect to t on [n, t) and letting t → n + 1, one can
obtain a system of difference equations

59
x(n + 1) = x(n)eμ γ ( )
,
y(n + 1) =
e μ ( ) βμ ( ) δ
βμ x(n) y(n) + δy(n) − μ x(n)y(n) − κ + κ
βμ x(n) + δ − μ x(n)
.
(7)
Computations give us that the positive equilibrium point of the system is
(x, y) =
⎝
⎜
⎜
⎜
⎛1 −
4βγκ + −4βδ + μ μ
μ μ
2β
,
μ
γ
⎠
⎟
⎟
⎟
⎞
.
Hereafter,
γ <
δμ
κ
and β ≤
μ μ
−4γκ + 4δμ
. (8)
The linearized system of (7) about the positive equilibrium point is w(n + 1) = Aw(n), where A
is a matrix as;
A =
⎝
⎜
⎜
⎜
⎛
1 −
γ(1 −
4βγκ + (−4βδ + μ )μ
μ μ
)
2β
e
γκ
μ
(−1 + e
γκ
μ
) μ μ
/
4βγκ + (−4βδ + μ )μ
γ κ
e
γκ
μ
⎠
⎟
⎟
⎟
⎞
. (9)
The characteristic equation of the matrix A is
p(λ) = λ + λ −1 − e
γκ
μ
+ e
γκ
μ
−
e
γκ
μ
(−1 + e
γκ
μ
)μ 4βγκ + (−4βδ + μ )μ (− μ μ + 4βγκ + (−4βδ + μ )μ )
2βγκ
. (10)
Now we can determine the stability conditions of system (7) with the characteristic equation (10).
Hence, we use following theorem that is called Schur-Chon criterion.
Theorem A ([20]). The characteristic polynomial
p(λ) = λ + p λ + p (11)
has all its roots inside the unit open disk (|λ| < 1) if and only if
(a) p(1) = 1 + p + p > 0,
(b) p(−1) = 1 − p + p > 0,

60
(c) D = 1 + p > 0,
(d) D = 1 − p > 0.
Theorem 1. The positive equilibrium point (x, y) of system (7) is local asymptotically stable if
δμ
κ + κμ
< γ <
δμ
κ
and β ≤
μ μ
−4γκ + 4δμ
.
Proof. From characteristic equations (10), we have
p = −1 − e
γκ
μ
,
p = e
γκ
μ
−
e
γκ
μ
(−1 + e
γκ
μ
2βγκ
.
From Theorem A/a we get
p(1) =
2βγκ − (−1 + e
γκ
μ
2βγκ
.
It can be shown that if
− μ μ + 4βγκ + (−4βδ + μ )μ < 0, (12)
then p(1) > 0. On the other hand, the inequality (12) always holds under the condition (8). When
we consider Theorem A/b and Theorem A/c with the fact (12), we have respectively
p(−1) = 2 + 2e
γκ
μ
−
e
γκ
μ
(−1 + e
γκ
μ
2βγκ
> 0
And
D = 1 + e
γκ
μ
−
e
γκ
μ
(−1 + e
γκ
μ
2βγκ
> 0.
From Theorem A/d, we get
D = e
γκ
μ
(−1 + e
γκ
μ
)(2βγκ + 4βγκμ + (−4βδ + μ )μ − μ μ 4βγκ + −4βδ + μ μ ).
By using the conditions of Theorem 1, we can also see that D > 0. This completes the proof.
Now we can use parameters value in Table 1 for the testing the conditions of Theorem 1. Using
these parameter values, it is observed that the positive equilibrium

61
point (x, y) = (7.41019,0.5599) is local asymptotically stable where blue and red graphs
represent x(n) and y(n) population densities respectively (see Figure 1).
Table 1. Parameters values used for numerical analysis
Parameters Numerical Values Ref
μ tumor growth parameter 0.5549 [8]
γ interaction rate 1 [8]
μ tumor stimulated proliferation rate 0.00484 [8]
β inverse threshold for tumor suppression 0.00264 [8]
δ death rate 0.37451 [8]
κ rate of influx 0.19
Figure 1. Graph of the iteration solution of x(n) and y(n), where x(1) = y(1) = 1
Theorem 2. Let {x(n), y(n)}∞
be a positive solution of the system. Suppose that
μ − γy(n) < 0, βx(n) − 1 > 0 and βμ x(n) y(n) + δy(n) − μ x(n)y(n) − κ < 0 for n =
0,1,2,3 …. Then every solution of (7) is bounded, that is,
x(n) ∈ (0, x(0)) and y(n) ∈ 0,
κ
δ
.
Proof. Since {x(n), y(n)}∞
> 0 and μ − γy(n) < 0, we have
x(n + 1) = x(n)eμ γ ( )
< x(n).
In addition, if we use βμ x(n) y(n) + δy(n) − μ x(n)y(n) − κ < 0 and βx(n) − 1 > 0, we have
y(n + 1) =
e μ ( ) βμ ( ) δ
y(n)(βμ x(n) + δ − μ x(n)) − κ + κ
μ x(n)(βx(n) − 1) + δ
0 50 100 150 200 250 300 350 400 450 500
0
1
2
3
4
5
6
7
n
x(n)
and
y(n)

62
<
κ
μ x(n)(βx(n) − 1) + δ
<
κ
δ
.
This completes the proof.
Theorem 3. Let the conditions of Theorem 1 hold and assume that
x <
1
2β
and y <
κ
2μ x(n)(βx(n) − 1) + 2δ
.
If
x(n) >
1
β
and y(n) >
κ
μ x(n)(βx(n) − 1) + δ
,
then the positive equilibrium point of the system is global asymptotically stable.
Proof. Let E = (x , y) is a positive equilibrium point of system (7) and we consider a Lyapunov
function V(n) defined by
V(n) = [E(n) − E] , n = 0,1,2 …
The change along the solutions of the system is
∆V(n) = V(n + 1) − V(n) = {E(n + 1) − E(n)}{E(n + 1) + E(n) − 2E}.
Let A = μ − γy(n) < 0 which gives us that y(n) >
μ
γ
= y. If we consider first equation in (7)
with the fact x(n) > 2x , we get
∆V (n) = {x(n + 1) − x(n)}{x(n + 1) + x(n) − 2x}
= x(n) e − 1 {x(n)e + x(n) − 2x} < 0.
Similarly, Suppose that A = βμ x(n) + δ − μ x(n) > 0 which yields x(n) >
β
. Computations
give us that if y(n) >
κ
and y(n) > 2 , we have
∆V (n) = {y(n + 1) − y(n)}{y(n + 1) + y(n) − 2y}
=
1 − e (κ − y(n)A )
A
y(n)A e + 1 + κ 1 − e − 2yA
A
< 0.
Under the conditions
x <
1
2β
and y <
κ
2μ x(n)(βx(n) − 1) + 2δ
,
we can write
x(n) >
1
β
> 2x and y(n) >
κ
A
=
κ
μ x(n)(βx(n) − 1) + δ
> 2y.

63
As a result, we obtain ∆V(n) = (∆V (n), ∆V (n)) < 0.
3. NEIMARK-SACKER BIFURCATION ANALYSIS
In this section, we discuss the periodic solutions of the system through Neimark-Sacker
bifurcation. This bifurcation occurs of a closed invariant curve from a equilibrium point in
discrete dynamical systems, when the equilibrium point changes stability via a pair of complex
eigenvalues with unit modulus. These complex eigenvalues lead to periodic solution as a result
of limit cycle. In order to study Neimark-Sacker bifurcation we use the following theorem that is
called Schur-Cohn criterion.
Theorem B. ([20]) A pair of complex conjugate roots of equation (11) lie on the unit circle and
the other roots of equation (11) all lie inside the unit circle if and only if
(a) p(1) = 1 + p + p > 0,
(b) p(−1) = 1 − p + p > 0,
(c) D = 1 + p > 0,
(d) D = 1 − p = 0.
In stability analysis, we have shown that Theorem B/a, Theorem B/b and Theorem B/c always
holds. Therefore, to determine bifurcation point we can only analyze Theorem B/d. Solving
equation d of Theorem B, we have κ = 0.0635352. Furthermore, Figure 2 shows that κ is the
Neimark-Sacker bifurcation point of the system with eigenvalues
λ , = |0.945907 ± 0.324439i| = 1, where blue, and red graphs represent x(n) and
y(n) population densities respectively.
As seen in Figure 2, a stable limit cycle occurs around the positive equilibrium point at the
Neimark-Sacker bifurcation point. This limit cycle leads to periodic solution which means that
tumor and immune cell undergo oscillations (Figure 3). This oscillatory behavior has also
occurred in continuous biological model as a result of Hopf bifurcation and has observed
clinically.
Figure 2. Graph of Neimark-Sacker bifurcation of system (7) for κ = 0.0635352. Initial conditions and
other parameters are the same as Figure 1
0 20 40 60 80 100 120 140 160 180 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
x(n)
y(n)

64
Figure 3. Graph of the iteration solution of x(n) and y(n) for κ = 0.063535. Initial conditions and other
parameters are the same as Figure 1
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0.1
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1.1
n
y(n)

65
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Authors
Senol Kartal is research assistant in Nevsehir Haci Bektas Veli University in Turkey. He is a
Phd student Department of Mathematics, University of Erciyes. His research interests
include issues related to dynamical systems in biology.
Fuat Gurcan received her PhD in Accounting at the University of Leeds, UK. He is a
Lecturer at the Department of Mathematics, University of Erciyes and International
University of Sarajevo. Her research interests are related to Bifurcation Theory, Fluid
Dynamics, Mathematical Biology, Computational Fluid Dynamics, Difference Equations and
Their Bifurcations. He has published research papers at national and international journals.

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