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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 152174, 13 pages
http://dx.doi.org/10.1155/2013/152174
Research Article

Modeling Complex Systems with Particles Refuge by Thermostatted Kinetic Theory Methods

Dipartimento di Scienze Matematiche, Politecnico, Corso Duca degli Abruzzi 24, 10129 Torino, Italy

Received 7 September 2013; Accepted 5 October 2013

Academic Editor: Luca Guerrini

Copyright © 2013 Carlo Bianca. 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

This paper is concerned with the mathematical modeling of complex systems characterized by particles refuge. Specifically the paper focuses on the derivation and moments analysis of thermostatted kinetic frameworks with conservative and nonconservative interactions for closed and open complex systems at nonequilibrium. Applications and future research perspectives are discussed in the last section of the paper.

1. Introduction

The development of nonlinear analysis methods and the strengthening of modern computers have allowed a more accurate description of complex systems in the applied sciences.

The difficulty in modeling complex systems hails from the unexpected behaviors that stem from interactions between the inner elements and the outer environment [1, 2]. These behaviors are not the mere sum of the whole interactions (the system is more than the sum of its component parts) and collective behaviors are also consequence of self-organization [3, 4].

The global collective behaviors that emerge in most complex systems in the applied sciences, such as self-organized biological systems, vehicular traffic, crowd and swarm dynamics, and social and economic systems, are usually in response to external actions. Indeed the environmental action or external agents can affect the whole dynamics and abrupt changes can occur; for example, the behavior pattern carried out by animal swarms can be disturbed by the attack of a predator; tumor growth can be controlled and avoided by the action of an external vaccine at the cellular scale. The interested reader in a deeper understanding of these topics is referred to papers [515] and the references cited therein.

In particular complexity arises in biology systems at different levels of organization that range from individual organisms to whole populations; see [16, 17]. Indeed a mutation occurring in particularly fortuitous circumstances can be amplified to the extent that it changes the course of evolution. Moreover the outer environment exerts an action that can influence the whole dynamics far more rapidly than what can be perceived.

Although the environmental action has an outstanding role in the whole dynamics of the system, only a few mathematical models and methods have been developed and used to model open complex systems of the biological and, in general, of the applied sciences systems.

Recently thermostatted kinetic theory for active particles methods have been proposed in [18] for the modeling of complex behaviors occurring in living systems; see also the review paper [19] and the analysis developed in [20, 21]. However, the above-mentioned thermostatted kinetic frameworks seem not suitable for the modeling of proliferative, destructive, and mutative events that occur in biological systems as consequence of the interactions among the constituent elements of the system.

This paper is concerned with a further generalization of the thermostatted kinetic frameworks proposed in paper [22]; the paper deals with the modeling of nonequilibrium complex systems characterized by conservative and nonconservative interactions (including mutative interactions, whose importance has been stressed by most scientists; see [23]) and particles refuge, namely, the capability of some particles to escape the interactions. Moreover the role of external actions or agents at the microscopic scale is taken into account, generating a more suitable thermostatted kinetic framework for the active particles, which can be proposed for the mathematical modeling of the time evolution of the inner system in the presence of the outer environment (open system).

To the best of our knowledge, the role of particles refuge has not been yet taken into account in thermostatted models; particles refuge is a prerogative of predator-prey models.

In the last three decades, the introduction of prey refuge in predator-prey models has gained much attention; see, among others, the mathematical analysis developed in papers [2435] and the review paper [36]. In the pertinent literature, prey refuge has been incorporated in predator-prey interactions for considering two types of events: those that protect a constant fraction of prey and those that protect a constant number of prey. The introduction of prey refuge is inserted for modeling the strategies that decrease the predation ability (spatial or temporal refuges, prey aggregations, or reduced search activity by prey). The presence of refuges may affect the coexistence of predators and prey, the stability of equilibrium solutions and could imply the existence of Hopf bifurcations.

The contents of the present paper are outlined as follows. After this introduction, Section 2 presents the mathematical setting of the thermostatted kinetic framework (TKF) for active particles, for the modeling of complex systems with conservative interactions and particles refuge; the systems are composed of a large number of active particles grouped into functional subsystems; the time evolution of each subsystem occurs in the absence of microscopic external actions (closed systems) and is depicted by a distribution function. The derivation of differential equations for the time evolution of the moments and the existence and uniqueness theorem of the mild solution are also performed in this section. Section 3 introduces the TKF for systems with proliferative/destructive and mutative interactions. This section shows how the moments evolution is influenced by these nonconservative interactions. Further generalizations are discussed in Section 4. The derivation of the thermostatted kinetic framework for open systems is presented and discussed in Section 5. Finally, Section 6 concludes the paper with applications and research perspective on the mathematical structures derived in the present paper.

2. The TKF with Conservative Interactions and Particles Refuge

This section is meant to derive the thermostatted kinetic framework for the modeling of complex systems subjected to external force fields such that some particles are able to refuge. Specifically the whole system is decomposed into a finite large number of particle subsystems such that each subsystem is composed of active particles, which are able to perform the same strategy (functional subsystems). Particles are able to interact with one another and with the particles of the other subsystems. The strategy expressed by the particles is modeled by inserting, into the microscopic state of the particles, a scalar variable , called activity variable. The time evolution of each functional subsystem is depicted by statistical representation, specifically by a distribution function , for .

Let be the vector function whose th component is the distribution function of the th functional subsystem, and the function defined as follows: Assuming that , the th-order moment of each functional subsystem reads In general represents the particles density of the th functional subsystem and the related activation energy. In particular the th-order moment of the whole system is obtained by summing the th-order moment of the subsystems:

In what follows we assume that the domain is a compact set of with respect to the usual topology. Moreover some particles of one or more functional subsystems are able to refuge during the interactions. In particular, for explanation convenience, we assume that some particles of the subsystem with distribution function escape the interaction; namely, no refuging particles have microscopic state .

For deriving the time evolution equation of each distribution function , we need to define the types of interactions. Mutual interactions refer to test particles, whose distribution function is denoted by , candidate particles (with distribution function denoted by ), and field particles (with distribution function denoted by ). Candidate particles can acquire in probability the microscopic state of the test particle after interactions with field particles. The possibility of interactions among the particles is measured by introducing the nonnegative function , which represents the interaction rate between the -particle of the subsystem and the -particle of the subsystem . In particular we model the particles refuge of the functional subsystem by choosing the interaction rate as follows: where and is the characteristic (indicator) function of . The probability that, after the interaction, the candidate particle undergoes a change in its microscopic state (that of test particle) is measured by introducing the following nonnegative function: which is assumed to be a probability density with respect to and then the following condition holds: Setting bearing all the above in mind and summing up with respect to all candidate and field particles we obtain the following operator which models the gain of test particles into the th functional subsystem: Similarly, the loss of test cells into the th functional subsystem is modeled by the operator that reads

We now assume that the system is closed from the microscopic point of view and in nonequilibrium conditions; namely, there is an external force field , for , at macroscopic scale. Bearing all the above in mind, the thermostatted kinetic framework with particles refuge for closed systems is obtained by balancing the inlet and the outlet flow of particles into the volume of the microscopic states. The framework is a system of kinetic equations coupled with the Gaussian isokinetic thermostat, whose th equation reads where is the thermostatted term, which reads The thermostatted term is a damping operator that is adjusted so as to control the evolution of lower th order moments (in general the and moments). This term is based on Gauss principle of the least constrain; see [3741].

Remark 1. It is worth stressing that the thermostatted term (11) can be written as function of the = 1st-order moment as follows: This is a further source of nonlinearities.

The depicted thermostatted kinetic framework (10) is quite general and can be exploited to originate specific models for complex systems with particles refuge by acting on the specific forms of the interaction rate , the probability density , and the external force .

It is worth stressing that particles refuge defined in the present paper can also be introduced in the thermostatted frameworks developed and analyzed in the paper [22] and in the -thermostatted framework proposed in the paper [42]. In the latter case the thermostatted term reads

2.1. Preliminary Investigations

This section deals with analytical results on the mathematical framework (10) related to the moment evolutions.

Definition 2. Let , , be an external force field differentiable with respect to ; the interaction rate between the th and th functional subsystems; , for , the function defined in (7); the probability density satisfying the property (6). A vector function , whose th component is the distribution function of the th functional subsystem , is said to be solution of the model (10) if(i);(ii) is differentiable with respect to the variables and ;(iii) is an integrable function with respect to the elementary measure ;(iv) is an integrable function with respect to the elementary measure ;(v) is an integrable function with respect to the elementary measure ;(vi) as ;(vii) satisfies (10) for all .

Assumption 3. We assume that and are nonnegative constants.

Assumption 4. We assume that is an even nonnegative function on , with .

Assumption 5. We assume that satisfies the following property: The following result holds true.

Lemma 6. Let be an odd number and assume that Assumptions 3 and 4 hold. Let be a nonnegative solution of the thermostatted kinetic framework with particles refuge (10). Then the th-order moment of satisfies the following ordinary differential equation: where Moreover if is initially bounded, it remains bounded for all .

Proof. The interaction operator can be written as follows: where is given by formula (16).
Multiplying both sides of (10) by and integrating over , we have Summing up with respect to , we obtain Performing integration by parts on the second and third terms of the left hand side of (10) and summing up with respect to , we have and the proof is gained.

Remark 7. Setting in formula (15), the 1st-order moment , which is part of the thermostat operator (11), is solution of the following Riccati nonlinear ordinary differential equation: Equation (21) admits a unique solution when an initial condition is assigned; then it is possible to obtain an explicit formula for ; see [20].

2.2. Existence of Mild Solutions

The possibility to obtain an explicit formula for allows introducing the notion of mild solution for the relative abstract Cauchy problem of the thermostatted kinetic framework with particles refuge (10) and performing the mathematical analysis developed in paper [43] regarding the existence and uniqueness of the mild solution.

Let be a -integrable vector function on such that . The Cauchy problem for the thermostatted framework (10) reads where is the following operator: with and . Bearing all the above in mind, the thermostatted framework in vectorial form can be rewritten as follows: where . By applying the following transformations: we have with and . Set Let be fixed. The integral form of (24) for and reads and then

Definition 8. Let be a -integrable function on such that . A nonnegative vector function is said to be a mild solution to the Cauchy problem (22) if and satisfies (29).
The following result holds true.

Theorem 9. Assume that Assumptions 3, 4, and 5 hold. Let be the initial vector function such that . Then the Cauchy problem (22) admits a unique globally in time mild solution. Furthermore .

Proof. The proof of the theorem follows by integration along the characteristic curves and the definition and analysis of successive approximations sequences; see [20].

Remark 10. Theorem 9 states that the introduction of the thermostatted term guarantees the conservation of the and moments, namely, the density and the activation energy of the system.

3. The TKF with Nonconservative Interactions and Particles Refuge

This section deals with the derivation and analysis of the TKF with particles refuge and nonconservative interactions, namely, interactions that modify the number of particles. Nonconservative interactions include proliferative, destructive, and mutative events. These interactions are typical of the biological systems; indeed proliferative/destructive events occur when cells start to duplicate or are eliminated/inhibited by the immune system; mutative events are the result of genetic mutations.

Following [17, 44] and references cited therein, the role of proliferative and/or destructive interactions during particle refuge is modeled by the following operator: where with being the net proliferation rate. In particular for the particles refuge case, the operator (30) reads Moreover the role of mutative events is modeled by the following operator: where with being the net proliferation rate into the th functional subsystem, due to interactions that occur with rate , of the candidate -particle, with state , and the field -particle, with state . In particular in the case of particles refuge, the operator (33) reads

Assuming that the system is closed and in nonequilibrium conditions, the th equation of the thermostatted kinetic framework with particles refuge for closed systems and with nonconservative interactions thus reads where the meaning of each operator can be recovered from the previous section.

3.1. Evolution of Moments

This section is concerned with a preliminary analysis of the thermostatted framework (36).

Definition 11. Let , , be an external force field differentiable with respect to ; the interaction rate among the subsystems; , for , the function defined in (7); the function defined in (31); the function defined in (34), for ; the probability density. A vector function , whose th component is the distribution function of the th functional subsystem , is said to be solution of model (36) if the conditions (i), (ii), (iii), (iv), and (v) of Definition 2 and the following further conditions hold:(vi) is an integrable function with respect to the elementary measure ;(vii) is an integrable function with respect to the elementary measure ;(viii) as ;(ix) satisfies (36) for all .

Assumption 12. In what follows we assume that , , , and are constants and is an even function with respect to , where with .

Remark 13. Under Assumption 12, if we also assume for computational convenience that , integrating the left and right hand sides of (36) with respect to , we obtain the following equation: which shows that the evolution of the density of the th functional subsystem depends on the distribution function of the nonrefuging particles. In particular the above equation can be rewritten as follows: where is the density of the particles of the functional subsystem that have refuged. Moreover Therefore the differential equation for the density of the th functional subsystem reads The following result holds true.

Theorem 14. Let be an odd number and assume that Assumption 12 holds. If there exists a nonnegative solution of the thermostatted kinetic framework with particles refuge and nonconservative interactions (36), then the th-order moment is solution of the following ordinary differential equation: where is given by formula (16).

Proof. The interaction operator can be written as follows: where Multiplying both sides of by and integrating over , we have Summing up with respect to , we obtain and bearing Lemma 6 in mind, we obtain the proof.

Remark 15. Setting in the differential equation (42), the 1st-order moment of satisfies the following Riccati nonlinear ordinary differential equation:

As (47) states, the time evolution of does not depend on the mutative interactions when is constant. If (namely, it does not depend on and ), the evolution equation of reads where Obviously, if is an even function with respect to , then and (48) does not depend on the mutative term again.

It is worth stressing that, in the general case , the differential equation fulfilled by depends on the following quantity: for which, in general, is not possible to give an explicit formula.

As already mentioned, the possibility to obtain an explicit formula for solution of the differential equation (47) allows defining the mild solution of the relative abstract Cauchy problem. However, in the nonconservative interactions case, global existence may not occur and the proof of the global existence depends casewise. This is a work in progress and results will be reported in due course.

4. Particles Refuge in Functional Subsystems

For concluding the discussion on the introduction of particles refuge in thermostatted kinetic models for closed systems, this section is concerned with the derivation of the thermostatted kinetic framework that generalizes the thermostatted kinetic framework (36) when more than one functional subsystem contains particles refuge. Specifically we define the following sets: Therefore the relative interaction rate defined in (4) now reads where denotes the domain of the nonrefuging particles of the th functional subsystem.

Bearing all the above in mind, we can split the operators in the right hand side of (36) as follows: the gain particles operator reads where The lost particles operator is splitted as follows: The proliferative operator reads and finally the mutative operator is written as follows:

5. The TKF for Open Systems with Particles Refuge

The mathematical structures dealt with the previous sections are meaningful for complex biological systems with particles refuge subjected to external force fields at the macroscopic scale but in the absence of interactions with the outer environment at the microscopic scale. Modeling external actions at the microscopic scale means representing the outer system as functional subsystems with distribution function denoted by ; see [45]. Specifically the th functional subsystem interacts with the th external agent, for . Therefore, the external agent is regarded as a specific functional subsystem with the ability to interact with active particles of the inner system and has the ability to modify the state of the system by a particular action related to the variable . This system is known as open system.

Assumption 16. It is assumed that the action is factorized as follows: where the term is the intensity that depends on time, by which the agent acts on the system, and is the probability function associated with the variable .

Let be the vector whose components are the distribution functions associated with the external agents. Thus the th equation of the thermostatted mathematical framework, with particle refuge and nonconservative interactions, for open systems reads with where the operator reads and the meaning of each operator can be recovered by the previous sections, and consider The terms of the operator have the following meanings:(i) is the inner-outer encounter rate between the th external agent, with state , and the active (candidate) particle of the th population, with state . According to the role of particles refuge, the inner-outer encounter rate reads (ii) is the inner-outer transition probability density which describes the probability density that a candidate particle of the th population, with state , falls into the state after an interaction with the th external agent whose state is .

The density satisfies, for all and , the following condition:

It is worth noting that the thermostatted framework (60) is not autonomous; indeed the time variable is explicitly inserted by the intensity function .

5.1. On the Evolution of Moments

This section is meant to derive evolution equations for the moments of the solution of the thermostatted framework (60).

Definition 17. Let , , be an external force field differentiable with respect to ; , the inner-inner and inner-outer interaction rate among the subsystems, for and ; , for , the function defined in (7); the function defined in (31); the function defined in (34), for ; the inner-inner probability density; the inner-outer probability density; the distribution function of the external actions.
A vector function , whose th component is the distribution function of the th functional subsystem , is said to be solution of model (60) if the conditions (i), (ii), (iii), (iv), and (v) of Definition 2 and the conditions (vi), (vii), (viii), and (ix) of Definition 11 hold, and(x) is an integrable function with respect to the elementary measure ;(xi) satisfies (60) for all .

Assumption 18. In what follows we assume that , , , , and are constants and and are even functions with respect to , where with . The following result holds true.

Theorem 19. Assume that Assumption 18 holds. If there exists a nonnegative solution of the thermostatted kinetic framework with particles refuge and nonconservative interactions (60), then the 1st-order moment is solution of the following Riccati nonlinear ordinary differential equation: where

Proof. The interaction operator can be written as follows: where Multiplying both sides of by and integrating over , we have Summing up with respect to , we obtain and the proof is gained.

It is worth stressing that, as in the previous sections, if is an odd number, we are able to obtain the differential equation for the moment. Specifically for open systems, the moment is solution of the following ordinary differential equation: It is easy to show that, if is initially bounded, it remains bounded for all .

6. Concluding Remarks and Applications

The goal of this paper is the derivation of mathematical frameworks for the modeling of complex biological system with particles refuge and characterization of proliferative and mutative interactions. Moreover the roles of external actions acting on the system at the macroscopic and microscopic scales have been taken into account. These actions can refer to the action of the external environment or agents. The mathematical frameworks proposed in the present paper also refer to complex systems characterized by nonequilibrium circumstances (due to the actions of external macroscopic force fields) and particles that escape from interactions (particle refuge).

The evolution equation satisfied by moments of the solution belongs to the class of linear differential equations (for odd number) and Riccati differential equations (for ). As known, the general form of the Riccati differential equation reads which is a nonlinear ordinary differential equation, where , , and are continuous functions defined on a subset of . The analytical method for solving Riccati equations of the form (73) is based on the knowledge of a particular solution. Indeed, let be a known solution of (73); then the general integral of (73) can be obtained as follows: with being the general integral of the following first-order linear ordinary differential equation: whose general integral reads

Therefore we are able to obtain an explicit formula of the moments when a specific complex system is modeled, considering, as particular solution can be taken, the critical point of the framework, which is in particular a constant solution; see [21].

Differential equations fulfilled by moments with order of an even number have not been derived in the present paper. Indeed, following the same strategy performed in the whole paper, we are not able to give an explicit formula to the following integrals: without adding further assumptions on the terms of the thermostatted framework.

As already mentioned, applications of the particles refuge introduction refer to the modeling of complex biological systems, especially to the tumor escape during tumor-immune system competition; see, among others, papers [4651]. As known, tumor escape occurs when the immune system response completely fails to control the tumor progression; the process results in the selection of tumor cell variants that are able to resist, avoid, or suppress the antitumor immune response, leading to the escape phase. During the escape phase, the immune system is no longer able to contain tumor progression, and a progressively growing tumor results; see, among others, papers [52, 53] and the references cited therein.

Research perspectives include the modeling of space dynamics [54] and the introduction of stochastic terms that model jump processes in the activity and/or in the velocity variable. Specifically in velocity-jump processes discontinuous changes in the speed or direction of an individual are generated by a Poisson process; see paper [55] and the references section. In particular the resulting thermostatted kinetic framework for each functional subsystem reads where the operator , which is responsible for the modeling by an activity-jump process, is written as follows: with being turning time and the turning kernel which gives, for each functional subsystem, the probability that the activity jumps into the activity if a jump occurs; the interaction frequency is defined as follows: A further research perspective consists in the formal derivation of macroscopic equations by means of asymptotic limits. Specifically these limits are obtained by employing the mathematical methods developed in papers [8, 5659] that use parabolic and/or hyperbolic scaling; see also the book [17]. Macroscopic equations are of great interest for the mathematical modeling at tissue scale. Indeed, they allow a complete micro/macrodescription [60]. This is part of the multiscale problem, which consists in linking the mathematical models derived at different scales; the interested reader is referred to the book [17].

The mathematical frameworks proposed in this paper established also interesting future research directions regarding the derivation of theoretical results. Indeed it is missing the proof of the existence and uniqueness of mild solution for the nonconservative thermostatted Cauchy problems. Moreover the existence of solutions to the stationary problem is missing. Stationary solutions satisfy the following system of thermostatted kinetic equations: where the meaning of each term can be recovered by the previous sections.

In this context, stationary solutions model nonequilibrium steady states. A nonequilibrium steady state is reached when the system is driven by external forces in a stationary nonequilibrium state where its properties do not change with time. The interested reader is referred to papers [19, 6163] and the references cited therein.

It is worth stressing that most of the complex biological systems are such that the interaction rate, the proliferative/destructive rate, the mutative rate, and the probability density are conditioned by the distribution functions of the functional subsystems and/or low-order moments. This is the case of the nonlinear interactions. However, the analysis of thermostatted kinetic models which include nonlinear interactions is still a hard open problem and a few number of contributions can be found in the pertinent literature; see [64].

Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The author acknowledges the support by the FIRB Project RBID08PP3J-Metodi matematici e relativi strumenti per la modellizzazione e la simulazione della formazione di tumori, competizione con il sistema immunitario, e conseguenti suggerimenti terapeutici.

References

  1. Y. Bar-Yam, Dynamics of Complex Systems, Studies in Nonlinearity, Westview Press, 1997. View at MathSciNet
  2. S. A. Levin, “Complex adaptive systems: exploring the known, the unknown and the unknowable,” American Mathematical Society Bulletin, vol. 40, no. 1, pp. 3–19, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  3. D. J. Krause and G. D. Ruxton, Living in Groups, Oxford University Press, Oxford, UK, 2002.
  4. D. L. Abel and J. T. Trevors, “Self-organization vs. self-ordering events in life-origin models,” Physics of Life Reviews, vol. 3, no. 4, pp. 211–228, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Helbing and P. Molnár, “Social force model for pedestrian dynamics,” Physical Review E, vol. 51, no. 5, pp. 4282–4286, 1995. View at Publisher · View at Google Scholar · View at Scopus
  6. R. L. Hughes, “The flow of human crowds,” Annual Review of Fluid Mechanics, vol. 35, pp. 169–183, 2003.
  7. C. Dogbe, “Nonlinear pedestrian-flow model: uniform well-posedness and global existence,” Applied Mathematics & Information Sciences, vol. 7, no. 1, pp. 29–40, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  8. C. Bianca and C. Dogbe, “A mathematical model for crowd dynamics: multiscale analysis, fluctuations and random noise,” Nonlinear Studies, vol. 20, pp. 349–373, 2013.
  9. C. Bianca and V. Coscia, “On the coupling of steady and adaptive velocity grids in vehicular traffic modelling,” Applied Mathematics Letters, vol. 24, no. 2, pp. 149–155, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  10. C.-J. Dalgaard and H. Strulik, “Energy distribution and economic growth,” Resource and Energy Economics, vol. 33, no. 4, pp. 782–797, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. R. M. Solow, “A contribution to the theory of economic growth,” Quarterly Journal of Economics, vol. 70, pp. 65–94, 1956.
  12. T. W. Swan, “Economic growth and capital accumulation,” Economic Record, vol. 32, pp. 334–361, 1956.
  13. C. Bianca and L. Guerrini, “On the Dalgaard-Strulik model with logistic populationgrowth rate and delayed-carrying capacity,” Acta Applicandae Mathematicae, 2013. View at Publisher · View at Google Scholar
  14. C. Bianca, M. Ferrara, and L. Guerrini, “Hopf bifurcations in a delayed-energy-based model of capital accumulation,” Applied Mathematics & Information Sciences, vol. 7, no. 1, pp. 139–143, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  15. C. Bianca, M. Ferrara, and L. Guerrini, “The Cai model with time delay: existence of periodic solutions and asymptotic analysis,” Applied Mathematics & Information Sciences, vol. 7, no. 1, pp. 21–27, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  16. F. Mazzocchi, “Complexity in biology. Exceeding the limits of reductionism and determinism using complexity theory,” EMBO Reports, vol. 9, no. 1, pp. 10–14, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Bianca and N. Bellomo, Towards a Mathematical Theory of Multiscale Complex Biological Systems, Series in Mathematical Biology and Medicine, World Scientific, River Edge, NJ, USA, 2011.
  18. C. Bianca, “Kinetic theory for active particles modelling coupled to Gaussian thermostats,” Applied Mathematical Sciences, vol. 6, no. 13–16, pp. 651–660, 2012. View at MathSciNet
  19. C. Bianca, “Thermostatted kinetic equations as models for complex systems in physics and life sciences,” Physics of Life Reviews, vol. 9, pp. 359–399, 2012.
  20. C. Bianca, “An existence and uniqueness theorem to the Cauchy problem for thermostatted-KTAP models,” International Journal of Mathematical Analysis, vol. 6, no. 17–20, pp. 813–824, 2012. View at MathSciNet
  21. C. Bianca, “Controllability in hybrid kinetic equations modeling nonequilibrium multicellular system,” The Scientific World Journal, vol. 2013, Article ID 274719, 6 pages, 2013. View at Publisher · View at Google Scholar
  22. C. Bianca, “Onset of nonlinearity in thermostatted active particles models for complex systems,” Nonlinear Analysis: Real World Applications, vol. 13, no. 6, pp. 2593–2608, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  23. M. A. Nowak, Evolutionary Dynamics: Exploring the Equations of Life, The Belknap Press of Harvard University Press, Cambridge, Mass, USA, 2006. View at MathSciNet
  24. A. Sih, “Prey refuges and predator-prey stability,” Theoretical Population Biology, vol. 31, no. 1, pp. 1–12, 1987. View at Publisher · View at Google Scholar · View at MathSciNet
  25. A. Sih, J. W. Petranka, and L. B. Kats, “The dynamics of prey refuge use: a model and tests with sunfish and salamander larvae,” American Naturalist, vol. 132, no. 4, pp. 463–483, 1988. View at Scopus
  26. H. Ylönen, R. Pech, and S. Davis, “Heterogeneous landscapes and the role of refuge on the population dynamics of a specialist predator and its prey,” Evolutionary Ecology, vol. 17, no. 4, pp. 349–369, 2003. View at Publisher · View at Google Scholar · View at Scopus
  27. T. K. Kar, “Stability analysis of a prey-predator model incorporating a prey refuge,” Communications in Nonlinear Science and Numerical Simulation, vol. 10, no. 6, pp. 681–691, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  28. V. Křivan, “On the Gause predator-prey model with a refuge: a fresh look at the history,” Journal of Theoretical Biology, vol. 274, pp. 67–73, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  29. J. Rebaza, “Dynamics of prey threshold harvesting and refuge,” Journal of Computational and Applied Mathematics, vol. 236, no. 7, pp. 1743–1752, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  30. Y. Tao, X. Wang, and X. Song, “Effect of prey refuge on a harvested predator-prey model with generalized functional response,” Communications in Nonlinear Science and Numerical Simulation, vol. 16, no. 2, pp. 1052–1059, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  31. X. Liu and Y. Xing, “Bifurcations of a ratio-dependent Holling-Tanner system with refuge and constant harvesting,” Abstract and Applied Analysis, vol. 2013, Article ID 478315, 10 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  32. S. Wang and Z. Ge, “The Hopf bifurcation for a pPredator-prey system with -logistic growth and prey refuge,” Abstract and Applied Analysis, vol. 2013, Article ID 168340, 13 pages, 2013. View at Publisher · View at Google Scholar
  33. X.-x. Qiu and H.-b. Xiao, “Qualitative analysis of Holling type II predator-prey systems with prey refuges and predator restricts,” Nonlinear Analysis: Real World Applications, vol. 14, no. 4, pp. 1896–1906, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  34. F. Chen, Z. Ma, and H. Zhang, “Global asymptotical stability of the positive equilibrium of the Lotka-Volterra prey-predator model incorporating a constant number of prey refuges,” Nonlinear Analysis: Real World Applications, vol. 13, no. 6, pp. 2790–2793, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  35. X. Guan, W. Wang, and Y. Cai, “Spatiotemporal dynamics of a Leslie-Gower predator-prey model incorporating a prey refuge,” Nonlinear Analysis: Real World Applications, vol. 12, no. 4, pp. 2385–2395, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  36. Z. Ma, W. Li, Y. Zhao, W. Wang, H. Zhang, and Z. Li, “Effects of prey refuges on a predator-prey model with a class of functional responses: the role of refuges,” Mathematical Biosciences, vol. 218, no. 2, pp. 73–79, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  37. D. J. Evans, W. G. Hoover, B. H. Failor, B. Moran, and A. J. C. Ladd, “Nonequilibrium molecular dynamics via Gauss's principle of least constraint,” Physical Review A, vol. 28, no. 2, pp. 1016–1021, 1983. View at Publisher · View at Google Scholar · View at Scopus
  38. D. J. Evans and G. P. Morriss, Statistical Mechanics of Nonequilibrium Fluids, Accademic Press, New York, NY, USA, 1990.
  39. O. G. Jepps and L. Rondoni, “Deterministic thermostats, theories of nonequilibrium systems and parallels with the ergodic condition,” Journal of Physics A, vol. 43, no. 13, Article ID 133001, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  40. G. P. Morriss and C. P. Dettmann, “Thermostats: analysis and application,” Chaos, vol. 8, no. 2, pp. 321–336, 1998. View at Scopus
  41. D. Ruelle, “Smooth dynamics and new theoretical ideas in nonequilibrium statistical mechanics,” Journal of Statistical Physics, vol. 95, no. 1-2, pp. 393–468, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  42. C. Bianca, M. Ferrara, and L. Guerrini, “High-order moments conservation in thermostatted kinetic models,” Journal of Global Optimization, 2013. View at Publisher · View at Google Scholar
  43. C. Bianca, “Modeling complex systems by functional subsystems representation and thermostatted-KTAP methods,” Applied Mathematics & Information Sciences, vol. 6, pp. 495–499, 2012.
  44. C. Bianca, “Mathematical modeling for keloid formation triggered by virus: malignant effects and immune system competition,” Mathematical Models and Methods in Applied Sciences, vol. 21, no. 2, pp. 389–419, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  45. I. Brazzoli, “From the discrete kinetic theory to modelling open systems of active particles,” Applied Mathematics Letters, vol. 21, no. 2, pp. 155–160, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  46. R. Eftimie, “Hyperbolic and kinetic models for self-organized biological aggregations and movement: a brief review,” Journal of Mathematical Biology, vol. 65, no. 1, pp. 35–75, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  47. R. Eftimie, J. L. Bramson, and D. J. D. Earn, “Interactions between the immune system and cancer: a brief review of non-spatial mathematical models,” Bulletin of Mathematical Biology, vol. 73, no. 1, pp. 2–32, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  48. C. Bianca and M. Pennisi, “The triplex vaccine effects in mammary carcinoma: a nonlinear model in tune with SimTriplex,” Nonlinear Analysis: Real World Applications, vol. 13, no. 4, pp. 1913–1940, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  49. C. L. Jorcyk, M. Kolev, K. Tawara, and B. Zubik-Kowal, “Experimental versus numerical data for breast cancer progression,” Nonlinear Analysis: Real World Applications, vol. 13, no. 1, pp. 78–84, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  50. M. Kolev, “Mathematical modelling of the competition between tumors and immune system considering the role of the antibodies,” Mathematical and Computer Modelling, vol. 37, no. 11, pp. 1143–1152, 2003. View at Publisher · View at Google Scholar · View at Scopus
  51. M. Kolev, A. Korpusik, and A. Markovska, “Adaptive immunity and CTL differentiation—a kinetic modeling approach,” Mathematics in Engineering, Science & Aerospace, vol. 3, pp. 285–293, 2012.
  52. R. T. Costello, J. A. Gastaut, and D. Olive, “Tumour escape from immune surveillance,” Archivum Immunologiae et Therapia Experimentalis, vol. 47, pp. 83–88, 1999.
  53. A. P. Vicari, C. Caux, and G. Trinchieri, “Tumour escape from immune surveillance through dendritic cell inactivation,” Seminars in Cancer Biology, vol. 12, no. 1, pp. 33–42, 2002. View at Publisher · View at Google Scholar · View at Scopus
  54. C. Bianca, “On the modelling of space dynamics in the kinetic theory for active particles,” Mathematical and Computer Modelling, vol. 51, no. 1-2, pp. 72–83, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  55. H. G. Othmer, S. R. Dunbar, and W. Alt, “Models of dispersal in biological systems,” Journal of Mathematical Biology, vol. 26, no. 3, pp. 263–298, 1988. View at Publisher · View at Google Scholar · View at MathSciNet
  56. T. Hillen and A. Stevens, “Hyperbolic models for chemotaxis in 1-D,” Nonlinear Analysis: Real World Applications, vol. 1, no. 3, pp. 409–433, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  57. T. Hillen and H. G. Othmer, “The diffusion limit of transport equations derived from velocity-jump processes,” SIAM Journal on Applied Mathematics, vol. 61, no. 3, pp. 751–775, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  58. A. Bellouquid and C. Bianca, “Modelling aggregation-fragmentation phenomena from kinetic to macroscopic scales,” Mathematical and Computer Modelling, vol. 52, no. 5-6, pp. 802–813, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  59. P. Degond and B. Wennberg, “Mass and energy balance laws derived from high-field limits of thermostatted Boltzmann equations,” Communications in Mathematical Sciences, vol. 5, no. 2, pp. 355–382, 2007. View at MathSciNet
  60. R. Balian, From Microphysics to Macrophysics. Methods and Applications of Statistical Physics, Springer, New York, NY, USA, 2007.
  61. B. Derrida, “Non-equilibrium steady states: fluctuations and large deviations of the density and of the current,” Journal of Statistical Mechanics, no. 7, Article ID P07023, 2007. View at MathSciNet
  62. A. Dickson and A. R. Dinner, “Enhanced sampling of nonequilibrium steady states,” Annual Review of Physical Chemistry, vol. 61, pp. 441–459, 2010. View at Publisher · View at Google Scholar · View at Scopus
  63. C. Kwon and A. Ping, “Nonequilibrium steady state of a stochastic system driven by a nonlinear drift force,” Physical Review E, vol. 84, no. 6, Article ID 061106, 2011. View at Publisher · View at Google Scholar · View at Scopus
  64. N. Bellomo, C. Bianca, and M. S. Mongiovì, “On the modeling of nonlinear interactions in large complex systems,” Applied Mathematics Letters, vol. 23, no. 11, pp. 1372–1377, 2010. View at Publisher · View at Google Scholar · View at MathSciNet