Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 430486, 11 pages

http://dx.doi.org/10.1155/2013/430486

Research Article

A Class of Solutions for the Hybrid Kinetic Model in the Tumor-Immune System Competition

Carlo Cattani¹ and Armando Ciancio²

¹Department of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano, Italy

²Depatment of Mathematics and Computer Science, University of Messina, Viale Ferdinando d'Alcontres 31, 98166 Messina, Italy

Received 6 March 2013; Accepted 7 April 2013

Academic Editor: Ezzat G. Bakhoum

Copyright © 2013 Carlo Cattani and Armando Ciancio. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

In this paper, the hybrid kinetic models of tumor-immune system competition are studied under the assumption of pure competition. The solution of the coupled hybrid system depends on the symmetry of the state transition density which characterizes the probability of successful occurrences. Thus by defining a proper transition density function, the solutions of the hybrid system are explicitly computed and applied to a classical (realistic) model of competing populations.

1. Introduction

In this paper, the two-scale tumor immune-system competition hybrid model [1–6] is studied under the assumption that the transition density function is a symmetric and separable function. The competition between tumor and immune-system can be modeled at different scales. Cells of different populations are characterized by biological functions heterogeneously distributed, and they are represented by some probability distributions. The interacting system is characterized at a macroscopic scale by a density distribution function which describes the cells activity during the interaction proliferation. At this level, the distribution of cells fulfills some partial differential equations taken from the classical kinetic theory. In this case, the more general model consists in a nonlinear system of partial differential equations. From the solution of this system, one can define a parameter which defines the time evolving distance between the two distributions, and this parameter is the charactering coefficient of the microscopic equations, typically an ordinary differential system for the competition of two populations.

This parameter has been considered [4, 5] as a random coefficient whose probability density distribution is modeled by the hiding-learning dynamics referred to biological events where tumor cells attempt to escape from immune cells which, conversely, attempt to learn about their presence.

Therefore, when the coupling parameter is obtained by solving the kinetic equations for the distribution functions, then it will be included in the classical Lotka-Volterra competition equations. We will analyze on a concrete example the influence of this stochastic parameter on the evolution. This method can be easily extended to more realistic competition models (see, e.g., [7–20]).

2. The Hybrid Model for the Tumor-Immune System Competition

Let us consider a physical system of two interacting populations, each one constituted by a large number of active particles with sizes:

for and .

Particles are homogeneously distributed in space, while each population is characterized by a microscopic state, called activity, denoted by the variable . The physical meaning of the microscopic state may differ for each population. We assume that the competition model depends on the activity through a function of the overall distribution:

The description of the overall distribution over the microscopic state within each population is given by the probability density function:

for , such that denotes the probability that the activity of particles of the th population, at the time , is in the interval :

Moreover, it is

We consider, in this section, the competition between two cell populations. The first one with uncontrolled proliferating ability and with hiding ability; the second one with higher destructive ability, but with the need of learning about the presence of the first population. The analysis developed in what follows refers to a specific case where the second population attempts to learn about the first population which escapes by modifying its appearance. The hybrid evolution equations specifically can be formally written as follows [4, 5]:

where , for , is a function of and acts over , while , for , is a nonlinear operator acting on and is a functional () which describes the ability of the second population to identify the first one. Then, (6) denotes a hybrid system of a deterministic system coupled with a microscopic system statistically described by a kinetic theory approach. In the following the evolution of density distribution will be taken within the kinetic theory.

The derivation of (6)₂ can be obtained starting from a detailed analysis of microscopic interactions. Consider binary interactions specifically between a test, or candidate, particle with state belonging to the th population and field particle with state belonging to the th population. The modelling of microscopic interactions is supposed to lead to the following quantities.

(i)The encounter rate, which depends for each pair of interacting populations on a suitable average of the relative velocity , with , .(ii)The transition density function , which is such that denotes the probability density that a candidate particle with activity belonging to the th population falls into the state , of the test particle, after an interaction with a field entity, belonging to the th population, with state . The transition density fulfills the condition

when and

The state transition

follows from the mutual action of the field particle () of the th population on the test particle () of the th population and vice versa so that

With respect to this mutual action, we can assume that this function depends on the biological model, as follows.

Competition within the first group and with others: particles of the th population interact with any other particle both from its own th population and from the th population so that

In this case, each particle of the th population can change its state not only due to the competition with the th population but also by interacting with particles of its own population. Instead, the individuals of the th population change their state only due to the interaction with the other th populations. They do not interfere with each other within their th group. Competition within the second group and with others: particles of the th population interact with any other particles both from its own th population and from the th population so that

Full competition within a group and with others: particles of each population interact with any other particles both from its own population and from the other population so that

Competition of two groups: particles of each population interact only with particles from the other population so that

We can assume that this kind of competition arises when the dynamics in each population are stable and each population behaves as a unique individual.

Then, by using the mathematical approach, developed in [1, 2], it yields the following class of evolution equations:

which can be formally written as (6)₂.

3. Transition Density Function Based on Separable Functions

In this section, we give the solution of (15) under some simple assumptions on the form of the transition density (7).

3.1. On the Symmetries of the State Transition Density

We assume that the integrability condition on ,

holds true. As a consequence, if we write the transition density as a linear combination of separable functions, this definition implies some symmetries which will be useful for the following computations, in particular.

Theorem 1. If one defines the transition density as

with , the following symmetry holds true:

Proof. From (7), (17), we have

There follows, with ,    and , ,

so that by a comparison of

to be valid for all , that is, as a consequence of the definition (17),

In particular, to fulfill (20), we can assume

from which, by taking into account (22), we get

so that, by a difference,

Thus, according to (25), the mutual action of the state transition given by the definition (7) can be summarized by (18).

Equations (17), (18) imply that the functions have to be carefully chosen so that (22), (24), and (18) are fulfilled.

In the following, we will consider a special choice for the transition density (17) as

so that (18) is fulfilled.

3.2. Preliminary Theorems

The special choice of , as defined in (26), enables us to explicitly solve (15); however, prior to computing the analytical solutions of (15), we need to show these preliminary theorems.

Theorem 2. Let be a function satisfying

and a given function for which

holds, then the equation

is solved by

where is the solution of the second kind homogeneous Fredholm integral equation

with   being the eigenvalue of the integral equation, and

when , is any arbitrary function fulfilling (32)₂.

Proof. Let us first notice that in the trivial case of , there is no dependence on the function

but this equation is also solved by (30) being

In the more general case, (31)₂, (32)₂ are direct consequence of the condition (5).

By a simple computation, (29) can be transformed into the Fredholm integral equations (31), (32).

In fact, by deriving (30), we have

so that (29), taking into account (30), becomes

that is,

from which (31), (32) and (30) hold true.

When , from the r.h.s, we have

so that cannot be univocally determined.

When the initial conditions are given, we have the following corollary.

Corollary 3. Let be a function satisfying (27) that is

and a given function for which

holds, then the solution of the initial value problem

is as follows:

,

, , .

The solution

exists only for ,

, . The solution

exists only for ,

, . For , the solution of (41) does not exist. When , the solution is

Proof. According to Theorem 2, the solution of (41)₁ is (30) with derivative (35). In the more general case, these two equations, at the initial time, give

having taken into account (41)₁.

The proof of all cases above is followed by solving these two equations in with respect to the initial condition .

For instance, for the first case , there follows

that is

so that (42) holds true.

When

which implies , from (49), we get a trivial solution of (29), (31), (32) and (47); that is,

Analogously, for the case system (47) becomes

However, if , the integral of the right side of the second equation is , while the integral of the first side must be zero.

With similar reasonings, we get the proof of the remaining cases.

4. Solution of the System (15)

In this section, we will give the explicit solution of the system (15) under some suitable hypotheses on both the encounter rate and the transition density . Let us assume the symmetry of so that

Thanks to the previous theorems, and the symmetry of as given by (18), system (15) simplifies, the following.

Theorem 4. Let the transition density be defined as

which fulfills (7) and the symmetries conditions (18), and the density function such that

holds. Equation (15) can be simplified into

Proof. By a substitution of (54) into (15), we get

that is,

from which,

There follows

According to (5), we get

Thus, we obtain, by a variable change,

so that (56) follows.

4.1. Pure Competition Model

We will consider the solution of (56) when, together with the hypotheses (53), (54)₂, some more conditions are given on the parameters.

According to (26), let us assume

together with the symmetries conditions (18).

If we define

we will discuss only the following hypotheses:

which seem to have some biological interpretations, being the pure encounter-competition model. This happens when the transition of state arises only when particles of one population interact only with an individual of the other population. In this case, individuals of one population do not interact with individuals of the same population.

Theorem 5. Let the transition density be defined as

with as given by (64), (65). This definition of the transition density fulfills (7) and the symmetries conditions (18). The density function is such that

By assuming

and for ,, the condition

system (56) becomes

and its solution is given by

Proof. From (56), we have

from which by linear combination, we get

With the above positions, we have

so that, by taking into account Theorem 2, it is

from which (72) follows.

Example 6. A transition density, which is compatible with this case, is the following:

with , Kronecker symbol.

5. Application to Lotka-Volterra Model

In this section, we will study a coupled system (6) where the macroscopic equations are the Lotka-Volterra equations (6)₁. Concerning the coupling stochastic parameter , we have to define the functional in (2), (6) depending on the “distance” between distributions; that is,

with

where the maximum learning result is obtained when the second population is able to reproduce the distribution of the first one: , while the minimum learning is achieved when one distribution is vanishing.

In some recent papers; it has been assumed [4, 5] that

In this case, it is , when ; otherwise with , depending on the time evolution of the distance between and .

Let us notice that is the coupling term which links the macroscopic model (6)₁ to the microscopic model (6)₂. There follows that the solution of the hybrid system (6) depends on the coupling parameter (80) which follows from the solution of (15). System (15) is a system of two nonlinear integrodifferential equations constrained by the conditions (7), (5). Moreover, its solution depends also the constant encounter rate , on the transition density function , and the initial conditions . In the following section, we will study the solution of (6), under some suitable, but not restrictive, hypotheses on .

Under the hypotheses of Theorem 5 and the solution (72), we have

Let us take

so that (72) are fulfilled. We have

that is

In the last case we have the usual Lotka-Volterra system, therefore, we will investigate the first case. Thus, according to (6), we have the system

with

The numerical solution of this system depends on both the parameters ,, and on the initial conditions , . We can see from Figures 1 and 2 that albeit the initial aggressive population is greater than , the first population can increase and keep nearly always over in the quasilinear case in Figure 1(a) or always under in presence of a strong nonlinearity in Figure 1(b).

(a)

(b)

Figure 1: Numerical solution of (plain) and (dashed) of the system (85) with initial conditions ,, for , and parameters , ((a) with parameters , ) and ((b) with parameters , ).

(a)

(b)

Figure 2: Numerical solution of (plain) and (dashed) of the system (85) with initial conditions , for and parameters ((a) with parameters , ) and ((b) with parameters , ).

If we invert the initial conditions so that the initial population of is greater than , we can see that in case of quasilinear conditions (see Figure 2(a)) the population after some short time becomes lower than . For a strong nonlinearity, instead after an initial growth , it tends to zero in a short time, while the second population grows very fast and becomes the prevalent population in Figure 2(b).

6. Conclusion

In this paper, the hybrid competition model has been solved under some assumptions on the transition density. In the simple case of Lotka-Volterra, the numerical solution gives some significant and realistic insights on the evolution of competing populations.

References

N. Bellomo, Modeling Complex Living Systems—Kinetic Theory and Stochastic Game Approach, Springer, Boston, Mass, USA, 2008.

A. Bellouquid and M. Delitala, Mathematical Modeling of Complex Biological Systems. A Kinetic Theory Approach, Springer,, Boston, Mass, USA, 2006.

C. Bianca and N. Bellomo, Towards a Mathematical Theory of Multiscale Complex Biological Systems, World Scientific, Singapore, 2010.

C. Cattani and A. Ciancio, “Qualitative analysis of second-order models of tumor-immune system competition,” Mathematical and Computer Modelling, vol. 47, no. 11-12, pp. 1339–1355, 2008.

C. Cattani and A. Ciancio, “Hybrid two scales mathematical tools for active particles modelling complex systems with learning hiding dynamics,” Mathematical Models and Methods in Applied Sciences, vol. 17, no. 2, pp. 171–187, 2007.

C. Cattani and A. Ciancio, “Separable transition density in the hybrid model for tumor-immune system competition,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 610124, 6 pages, 2012.

D. Abrams, “The evolution of predator-prey systems: theory and evidence,” Annual Review of Ecology, Evolution, and Systematics, vol. 31, Article ID 79105, 2000.

H. P. de Vladar and J. A. González, “Dynamic response of cancer under the influence of immunological activity and therapy,” Journal of Theoretical Biology, vol. 227, no. 3, pp. 335–348, 2004.

A. d'Onofrio, “A general framework for modeling tumor-immune system competition and immunotherapy: mathematical analysis and biomedical inferences,” Physica D, vol. 208, no. 3-4, pp. 220–235, 2005.

A. d'Onofrio, “Metamodeling tumor-immune system interaction, tumor evasion and immunotherapy,” Mathematical and Computer Modelling, vol. 47, no. 5-6, pp. 614–637, 2008.

U. Fory's, “Marchuk's model of immune system dynamics with application to tumor growth,” Journal of Theoretical Medicine, vol. 4, no. 1, pp. 85–93, 2002.

M. Galach, “Dynamics of the tumor-immune system competition—the effect of time delay,” International Journal of Applied Mathematics and Computer Science, vol. 13, no. 3, pp. 395–406, 2003.

R. A. Gatenby, T. L. Vincent, and R. J. Gillies, “Evolutionary dynamics in carcinogenesis,” Mathematical Models & Methods in Applied Sciences, vol. 15, no. 11, pp. 1619–1638, 2005.

S. A. Gourley and Y. Kuang, “A stage structured predator-prey model and its dependence on maturation delay and death rate,” Journal of Mathematical Biology, vol. 49, no. 2, pp. 188–200, 2004.

M. Kolev, “A mathematical model of cellular immune response to leukemia,” Mathematical and Computer Modelling, vol. 41, no. 10, pp. 1071–1081, 2005.

V. A. Kuznetsov, I. A. Makalkin, M. A. Taylor, and A. S. Perelson, “Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis,” Bulletin of Mathematical Biology, vol. 56, no. 2, pp. 295–321, 1994.

N. V. Stepanova, “Course of the immune reaction during the development of a malignant tumour,” Biophysics, vol. 24, no. 5, pp. 917–923, 1979.

O. Sotolongo-Costa, L. Morales Molina, D. Rodríguez Perez, J. C. Antoranz, and M. Chacón Reyes, “Behavior of tumors under nonstationary therapy,” Physica D, vol. 178, no. 3-4, pp. 242–253, 2003.

T. E. Wheldon, Mathematical Models in Cancer Research, Hilger, Boston, Mass, USA, 1988.

H. R. Thieme, Mathematics in Population Biology, Princeton, NJ, USA, 2003.