Research Article  Open Access
Dynamical Behaviour of a TumorImmune System with Chemotherapy and Optimal Control
Abstract
We have considered a tumor growth model with the effect of tumorimmune interaction and chemotherapeutic drug. We have considered two immune components—helper (resting) Tcells which stimulate CTLs and convert them into active (hunting) CTL cells and active (hunting) CTL cells which attack, destroy, or ingest the tumor cells. In our model there are four compartments, namely, tumor cells, active CTL cells, helper Tcells, and chemotherapeutic drug. We have discussed the behaviour of the solutions of our system. The dynamical behaviour of our system by analyzing the existence and stability of the system at various equilibrium points is discussed elaborately. We have set up an optimal control problem relative to the model so as to minimize the number of tumor cells and the chemotherapeutic drug administration. Here we used a quadratic control to quantify this goal and have considered the administration of chemotherapy drug as control to reduce the spread of the disease. The important mathematical findings for the dynamical behaviour of the tumorimmune model with control are also numerically verified using MATLAB. Finally, epidemiological implications of our analytical findings are addressed critically.
1. Introduction
It is well known that cancer is one of the greatest killer diseases in the world. Cancer, known medically as a malignant neoplasm, is characterized by an abnormal growth of cells. In cancer, cells divide and grow uncontrollably forming malignant tumors and invade nearby parts of the human body. The cancer may also spread to more distant parts of the body through the lymphatic system or bloodstream. Not all tumors are cancerous. The tumors which do not grow uncontrollably, do not invade neighbouring tissues, and do not spread throughout the body are not cancerous. There are over 200 different cancers including breast cancer, skin cancer, lung cancer, ovarian cancer, brain cancer, colon cancer, prostate cancer, and lymphoma and cancer. that afflict humans. Cancer symptoms vary widely based on the type of cancer.
Cancer immunology is the study of interactions between the immune system and cancer cells. It is also a growing field of research that aims to discover innovative cancer immunotherapies to treat and retard progression of this disease. In the last few decades, immunotherapy has become a significant part of treating several types of cancer. Human immune system is a collection of organs, special cells, and substances that help to protect from infections and some other diseases. Immune system cells and the substances they make travel through the body to protect it from pathogens (germs) causing infections. They also help to protect from cancer in some ways.
Pathogens (viruses, bacteria, and parasites) are foreign armies as they are not normally found in the body. They try to invade human body to use its resources to serve their own purposes and so they can hurt the body in the process. In fact, people often use the word foreign to describe invading germs or other substances not normally found in the body. The immune system is acting as body’s defense force. It helps keep invading germs out or helps kill them if they do get into the body. The immune system basically works by keeping track of all of the substances normally found in the body, and any new substance in the body that the immune system does not recognize raises an alarm to attack it. Substances that cause an immune system response are known as antigens. The immune response can lead to destruction of anything containing the antigen, such as pathogens or cancer cells. Pathogens (viruses, bacteria, and parasites) have substances on their outer surfaces, such as certain proteins, that are not normally found in the human body. The immune system identifies these foreign substances as antigens. Cancer cells are also different from normal cells in the body, and they often have unusual substances on their outer surfaces that can act as antigens. But the immune system is much better at recognizing and attacking pathogens than cancer cells. Pathogens are very different from normal human cells and are often easily identified as foreign whereas cancer cells and normal cells have fewer clear differences. Due to this reason the immune system may not always recognize cancer cells as foreign. Cancer cells are less like soldiers of an invading army and more like betrayers within the ranks of the human cell population. So, the immune system’s normal ability to attack cancer is limited, and this is the reason that many people with healthy immune systems still develop cancer. Sometimes the immune system recognizes the cancer cells, but the response may not be strong enough to kill them. Also, cancer cells themselves may give off substances that keep the immune system in check.
To overcome this, scientists have designed ways to help the immune system to recognize cancer cells and strengthen its response so that it will destroy them. The immune response including the recognition of cancer specific antigens is of particular interest in this field as knowledge gained drives the development of new vaccines and antibody therapies. Cellmediated immunity involves the production of cytotoxic Tlymphocytes (CTLs), activated macrophages, and release of various cytokines in response to an antigen.
A cytotoxic Tcell (CTL) is a Tlymphocyte (a type of white blood cell) that kills cancer cells, cells that are infected or cells that are damaged in other ways. Recently, increasing importance is being given to the stimulation of a CD4+ T helper cell response in cancer immunotherapy. T helper cells are a subgroup of lymphocytes, a type of white blood cell, that play an important role in the immune system, particularly in the adaptive immune system. They help in the activity of other immune cells by releasing Tcell cytokines. They assist other white blood cells in immunologic processes, including maturation of Bcells into plasma cells and memory Bcells and activation of cytotoxic Tcells (CTLs) and macrophages.
Cancer treatment includes chemotherapy, radiation therapy, immunotherapy, surgery, and monoclonal antibody therapy. The choice of therapy depends upon the location and grade of the tumor and the stage of the disease, as well as the general state of the patient. A number of experimental cancer treatments are also under development. Complete removal of the cancer without damage to the rest of the body is the goal of treatment. Sometimes this can be accomplished by surgery, but the propensity of cancers to invade adjacent tissue or to spread to distant sites by microscopic metastasis often limits its effectiveness. Chemotherapy and radiotherapy unfortunately have a negative effect on normal cells.
Chemotherapy is the treatment of cancer with one or more cytotoxic antineoplastic drugs (“chemotherapeutic agents”) as part of a standardized regimen. Traditional chemotherapy drugs act by killing cells that divide rapidly, one of the main properties of the most cancer cells. This means that chemotherapy also harms cells that divide rapidly under normal circumstances: cells in the bone marrow, digestive tract, and hair follicles. This results in the most common side effects of chemotherapy: myelosuppression (decreased production of blood cells), hence, also immunosuppression, mucositis (inflammation of the lining of the digestive tract), alopecia (hair loss), and so forth. Some newer anticancer drugs (e.g., various monoclonal antibodies) are not indiscriminately cytotoxic, but rather target proteins that are abnormally expressed in cancer cells and that are essential for their growth. Such treatments are often referred to as targeted therapy (as distinct from classic chemotherapy) and are often used alongside traditional chemotherapeutic agents in antineoplastic treatment regimens.
Theoretical study of tumorimmune dynamics is very useful. Mathematical modelling in tumor growth has helped to shape our understanding of tumorimmune dynamics. Kuznetsov and Knott [1] have developed a deterministic model that describes the interplay of the cancer cells and the cytotoxic killer cells. Though they have considered only one immune cell population, they have discussed effectively the mechanisms of tumor growth, suppression, and regrowth. Kuznetsov et al. [2] presented a mathematical model of the cytotoxic Tlymphocyte response to the growth of an immunogenic tumor. Through mathematical modelling Kirschner and Panetta [3] have illustrated the dynamics between tumor cells, immune cells, and interleukin2. Kolev [4] presented a mathematical model, showing competition between tumor cells and immune cells considering the role of antibodies. De Pillis et al. [5] presented a mathematical model on tumor growth using mixed immunotherapy and chemotherapy. De Pillis and Radunskaya [6] presented a mathematical model, showing competition between normal cells and tumor cells considering the role of chemotherapeutic drug. There are some other research works on tumorimmune dynamics [7–18]. A more common problem is found in the literature which minimizes the tumor volume at a final time subject to toxicity constraints [19, 20]. There are some researchers who have worked on the tumor growth models with optimal control strategies [6, 20–26]. These are very helpful to predict the most effective therapy and strategy to control the spread of diseases minimizing total drug administered. Ledzewicz and Schättler [27] presented a complete solution for a mathematical model for tumor antiangiogenesis for the problem of optimally scheduling a given amount of inhibitors in order to minimize the primary tumor volume.
In this paper, we have considered a tumor growth model together with the effect of tumorimmune interaction and chemotherapeutic drug. We have considered two immune components: (i) helper (resting) Tcells which are not able to attack and destroy tumor cells directly but release interleukin2 which stimulates CTLs and convert them into active (hunting) CTL cells and (ii) active (hunting) CTL cells which attack, destroy, or ingest the tumor cells. In our model there are four compartments, namely, tumor cells, active CTL cells, helper Tcells, and chemotherapeutic drug. The model construction and assumptions are described in Section 2. It should be mentioned here that the recent trend is to incorporate a more complex biochemistry [28]. Besides tumor cells and the drug, two species of Tlymphocytes (TLs) enter the model: helper (resting) TLs and active (hunting) cytotoxic TLs, which actually destroy the tumor cells. This is an oversimplification, since in the model the two types of Tcells are supposed to interact by direct contact, ignoring the role of mediators (interleukin2 and interferon alpha). However, dealing with a simplified model allows to give us clear insights and to get some results that may hopefully have some applicability. In Section 3, we have discussed the behaviour of the solutions of our system. The dynamical behaviour of our system by analyzing the existence and stability of the system at various equilibrium points is discussed in Section 4. In the next section, we have set up an optimal control problem relative to the model so as to minimize the number of tumor cells and the chemotherapeutic drug administration. Here we have used a quadratic control to quantify this goal and considered the administration of chemotherapeutic drug as control to reduce the spread of the disease. The quadratic control reflects the severity of the side effects of the drug imposed [29, 30]. When chemotherapeutic drugs are administered in high dose, they are toxic to the human body, which justifies the use of quadratic control. The important mathematical findings for the dynamical behaviour of the tumorimmune model with control are also numerically verified using MATLAB in Section 6. Finally, Section 7 contains the general discussions and conclusions of the paper and epidemiological implications of our mathematical findings.
2. Mathematical Model
In this section we have constructed a mathematical model of tumor growth with an immune response and chemotherapy.
The model can be presented by the following set of ordinary differential equations: with initial conditions where , , are the numbers of tumor cells, active CTL cells (hunting CTL cells), and helper Tcells (resting Tcells), respectively, and is the density of chemotherapeutic drug at time . All the model parameters , , , , , , , , , , , are positive constants.
The model parameters are described as follows: : per capita growth rates of tumor cells and helper (resting) Tcells, respectively; : reciprocal carrying capacities for tumor cells and helper (resting) Tcells, respectively; : rate of loss of tumor cells due to encounter with the active (hunting) CTL cells and rate of loss of active (hunting) CTL cells due to encounter with the tumor cells, respectively; : rate of conversion of helper (resting) Tcells to active (hunting) CTL cells; : per capita decay rate of active (hunting) CTL cells; : per capita decay rate of the chemotherapeutic drug; : the dose of chemotherapeutic drug given; : response coefficients to the chemotherapy drug for tumor cells, active (hunting) CTL cells, and helper (resting) Tcells, respectively.
This model involves certain assumptions which consist of the followings. The tumor cell population is assumed to grow logistically in the absence of active (hunting) CTL cells and chemotherapeutic drug. The tumor cells are being destroyed at a rate proportional to the product of densities of tumor cells and active (hunting) CTL cells. There is a loss in the active CTL cells due to encounters of tumor cells which is assumed to be proportional to the product of the densities of tumor cells and active CTL cells. Helper Tcells are converted into active CTL cells either by direct contact with them or by contact with cytokines produced by the helper Tcells according to the law of mass action. The helper Tcell population is also assumed to grow logistically in the absence of active CTL cells and chemotherapeutic drug. Chemotherapeutic drug destroys tumor cells as well as helper Tcells and active CTL cells; that is, chemotherapeutic drug has a negative effect on both tumor cells and immune cells.
3. Behaviour of the Solutions of System (1)
Theorem 1. Every solution of system (1) with initial conditions (2) exists in the interval and , , , , for all .
Proof. Since the righthand side of system (1) is completely continuous and locally Lipschitzian on , the solution of (1) with initial conditions (2) exists and is unique on , where [31]. From system (1) with initial conditions (2), we have which completes the proof.
Theorem 2. , , of system (1) subject to initial conditions (2) are bounded but may be bounded under some conditions among the parameters and the bounds of , , for .
Proof. From the first equation of system (1) it follows that
From the standard Kamke comparison theory [15], we get
Similarly, from the third equation of system (1) it follows that
From the standard Kamke comparison theory, we get
From the fourth equation of system (1), we get
Therefore,
Now, from the second equation of system (1) it follows that
Let us assume that , , , , , as , , and are bounded.
Now,
Therefore,
So,
Therefore, we can conclude that may be bounded under some conditions among the parameters and the bounds of , , for .
Hence, the theorem.
4. Equilibrium Points: Their Existence and Stability
In this section we will study the existence and stability behaviour of the system (1) at various equilibrium points. The equilibrium points of the system (1) are trivial equilibrium: , tumor persistent equilibrium:, where , ;, where , , ; tumor free equilibrium:, where , ,, where , , ; coexisting equilibrium: , where
Trivial Equilibrium. Now, the variational matrix of system (1) at is given by Therefore, eigenvalues of the characteristic equation of are , , , . It is clear that , are negative. Now, is stable if and ; that is, and , which implies that and .
Tumor Persistent Equilibrium. Consider the following exists only when ; that is, .Now, the variational matrix of system (1) at is given by Therefore, eigenvalues of the characteristic equation of are , , , . It is clear that , , are negative. Now, is stable if ; that is, which implies that . exists only when and ; that is, and .Now, the variational matrix of system (1) at is given by Therefore, eigenvalues of the characteristic equation of are , , , . It is clear that , , are negative. Now, is stable if ; that is, which implies that .
From the previous discussion we come to the following result.
Theorem 3. The tumor persistent equilibrium of the system (1) exists and is locally asymptotically stable if and another tumor free equilibrium of the system (1) exists and is locally asymptotically stable if
Tumor Free Equilibrium. Consider the following exists only when ; that is, .Now, the variational matrix of system (1) at is given by Therefore, eigenvalues of the characteristic equation of are , , , . It is clear that , are negative. Now, is stable if and ; that is, and , which implies that and . exists only when .Now, the variational matrix of system (1) at is given by Therefore, eigenvalues of the characteristic equation of are , and the solution of the quadratic equation where Now, it is easily noted that as and , has negative real roots. Therefore, is stable only when ; that is, .
From the previous discussion we come to the following result.
Theorem 4. The tumor free equilibrium of the system (1) exists and is locally asymptotically stable if and another tumor free equilibrium of the system (1) exists and is locally asymptotically stable if
Coexisting Equilibrium. exists when , , , or , , , .
Now, the variational matrix of system (1) at is given by
Therefore, eigenvalues of the characteristic equation of are and the solution of the following equation, where
By the RouthHurwitz criterion [32], it follows that the roots of (27) have negative real part if and only if
Now, it is easy to be noted that and if . Now, if then will be locally asymptotically stable. So, we came to the following result.
Theorem 5. The coexisting equilibrium of the system (1) exists and is locally asymptotically stable if , , , , condition (30) is satisfied.
Observations. The equilibrium points for the system without chemotherapeutic drug are, , , , ,, where
Using the parameter values given in Table 1 [2, 33–36] we get that the equilibrium points , , become unstable and does not exist. The only stable equilibrium point is . Here we are interested in the tumor free equilibrium . The tumor free equilibrium becomes locally asymptotically stable when , where the same tumor free equilibrium without drug, that is, , is locally asymptotically stable when .

Using the parameter values given in Table 1, we get Therefore, we get which implies that using the parameter values given in Table 1 the tumor free equilibrium with chemotherapeutic drug, that is, becomes locally asymptotically stable whereas the tumor free equilibrium without drug, that is, becomes unstable (using the same parameter values). This reasonably implies that if the chemotherapeutic drug is turned on then the tumor free equilibrium becomes stable where it is unstable without drug.
5. Epidemic Model with Control
In the context of mathematical modelling in cancer growth with chemotherapy, it is essential to frame an optimal control problem so that the total amount of drug used is minimized. This is done because of the implicit understanding that chemotherapy has damaging side effects. We have considered the tumor growth model (1). Now let us assume that the dose of chemotherapeutic drug is given as a function of time denoted by . We will use as a control to decrease the tumor burden minimizing total drug administered. Here we consider a quadratic control to quantify this goal. Therefore, our tumor growth model with control becomes satisfying The objective functional [16, 29, 30, 37–40] is defined as where , are positive constants representing the weights of the terms. The first term represents number of cancer cells and the second term represents harmful effects of drug on body. The square of the control variable () reflects the severity of the side effects of the drug imposed [29, 30]. When chemotherapeutic drugs are administered in high dose, they are toxic to the human body, which justifies the quadratic terms in the functional. Here the functional given in (36) should be minimized. So, we seek an optimal control such that where is the admissible control set.
5.1. Existence of an Optimal Control
Consider the following.
Theorem 6. Given the objective functional where subject to the system (34) with (35), then there exists an optimal control such that , if the following conditions [41] are satisfied. The class of all initial conditions with a control in the admissible control set along with each state equation being satisfied is not empty. The admissible control set is closed and convex. Each righthand side of the state system (34) is continuous and is bounded above by a sum of the bounded control and the state and can be written as a linear function of with coefficients depending on time and the state.The integrand of is convex on and is bounded below by with .
Proof. In order to verify the first condition, we use a result by Lukes ([42], Theorem ) for the system (34) with bounded coefficients. The control set is convex and closed by definition, which gives the condition . The righthand side of the state system (34) satisfies condition as the state solutions are a priori bounded.
We let be the righthand side of the system (34) without and let with Using the boundedness of the solutions, we see that where depends on the coefficients of the system.
For the fourth condition we need to show that where , are distinct elements of and . Now, Since implies that and , the expression , which implies that Lastly, which gives as a lower bound of .
Therefore, we can conclude that there exists an optimal control such that
5.2. Characterization of the Optimal Control
In order to derive the necessary conditions for the optimal control, Pontryagin’s Maximum Principle [43] is invoked.
The Hamiltonian is defined as follows: where , are the adjoint functions to be determined suitably.
The form of the adjoint equations and transversality conditions are standard results from Pontryagin’s Maximum Principle [43]. The adjoint system can be obtained as follows: The transversality conditions (or boundary conditions) are
By the optimality condition, we have By using the bounds for the control , we get
In compact notation,
Using (52) we obtain the following optimal system: subject to the following conditions:
The previous analysis can be summarized in the following theorem.
Theorem 7. There exist an optimal control and corresponding solutions that minimize over U. The explicit optimal controls are connected to the existence of continuous specific functions , the solutions of the following adjoint system: subject to the transversality conditions: Furthermore, the following property holds:
6. Numerical Simulations
Analytical studies can never be completed without numerical verification of the derived results. In this section we present computer simulation of some important analytic results of our system discussed earlier. Beside verification of our analytical findings, these numerical simulations are very important from practical point of view.
The optimal system has been solved numerically and the results have been presented graphically. This optimal system is a twopoint boundary value problem with separated boundary conditions at times and . Here, we have solved this twopoint boundary value optimality problem for . The value is chosen to represent the time in days at which treatment is stopped. An efficient method to solve twopoint BVPs numerically is collocation. A convenient collocation code is the solver BVP4c implemented under MATLAB, which can be used to solve nonlinear twopoint BVPs. To solve our BVP we have used collocation method with collocation code solver BVP4c. It is a powerful method to solve the twopoint BVP resulting from the optimality conditions.
The different variables (populations and control functions) in the objective functional given in (36) have different scales. Hence, they are balanced by choosing weight constants , in the objective functional given in (36). The numerical results for the optimal problem are obtained by using the parameter values given in Table 2 [2, 33–36]. At first we search for the optimal control function . This optimal control function is designed in such a way that it minimizes the objective functional given by (36), that is, minimizes the number of tumor cells and the chemotherapeutic drug administration. In Figure 1 we have presented the time series diagrams of tumor cells, active CTL cells, and helper Tcells without any control (). In Figure 2 we have presented the time series diagrams of tumor cells, active CTL cells, and helper Tcells with control ().

As it is depicted in Figures 1 and 2, the tumor cell population () level obtained using chemotherapeutic drug control is lower than its counterpart which results from practicing without control. From this observation we can conclude that the optimal control is much more effective for reducing the number of tumor cells to near zero. In perspective, one could conclude from the optimal control diagram (Figure 3) that we should give full effort in chemotherapeutic drug control in the beginning of the disease to reduce the spread of tumor cells. This means that chemotherapeutic drug is very much important in the beginning of the disease than when the disease prevails. From Figure 4 we observe that the chemotherapeutic drug control function () minimizes the objective functional given in (36). Overall the numerical analysis demonstrates that the control decreases the tumor burden minimizing total drug administered. Numerical simulations also agreed with the theoretical characterization of the optimal control.
7. Discussions and Conclusions
In this paper, we have considered a malignant tumor growth model together with the effect of tumorimmune interaction and chemotherapeutic drug. Here we have explored the effects and interactions of tumor cells and CTL immune cells through a system of nonlinear differential equations. We have considered the effects of chemotherapeutic drug on the tumor cells as well as immune cells. Two types of CTL immune cells enter the model: (i) helper (resting) Tcells which are not able to attack and destroy tumor cells directly but release interleukin2 which simulates CTLs and convert them into active (hunting) CTL cells and (ii) active (hunting) CTL cells which attack, destroy, or ingest the tumor cells. Next we have discussed dynamical behaviour of our system by analyzing the existence and stability of our system at various equilibrium points. For example, we have taken a set of estimated parameter values. Using them we have found that only one tumor free equilibrium becomes stable whereas the same equilibrium point becomes unstable without any chemotherapeutic drug. This sufficiently implies the necessity of using chemotherapeutic drug into the system.
The main focus of this paper is to set up an optimal control problem related to the model so as to minimize the number of tumor cells. We have considered the administration of chemotherapeutic drug as control to reduce the spread of the disease. Here we have used a quadratic control to quantify this goal. The quadratic control reflects the severity of the side effects of the drug imposed [29, 30]. When chemotherapeutic drugs are administered in high dose, they are toxic to the human body, which justifies the use of quadratic control. The control function is designed in such a way that minimizes the objective functional or cost function as given in (36).
The important mathematical findings for the dynamical behaviour of the tumorimmune model with control are also numerically verified using MATLAB. The graphical representations of the model with control as well as without control are presented for tumor cells and two types of immune cells so that we can compare them and can understand the effectiveness of using the control. It is observed that the optimal control is much more effective for reducing the number of tumor cells to near zero. Overall the numerical analysis demonstrates that a burst of treatment at the beginning is the best way to fight against the tumor cells. Numerical simulations agreed with the theoretical characterization of the optimal control.
The mathematical models on diseases are rather simple, but, nevertheless, they give insight into some of the consequences of public health policies. Our model formulation is based on the effects and interactions of tumor cells and immune cells and also the effects of chemotherapeutic drug on both tumor cells and CTL immune cells. We have also considered a model with control where the administration of chemotherapeutic drug is treated as control. Our model can provide an approximate estimation of timing and dosage of therapy that would be the best complement of the patient’s own defense mechanism versus the tumor cells. As with many models, the mathematical model presented in this paper should be treated with circumspection due to the assumptions made and the difficulties in the estimation of the model parameters. Most of the parameters are dependent on many factors, so they are rarely constants. But for the simplification of the system, these parameters are taken as constants. The activation of CTL cells, attacking and destroying tumor cells, is not instantaneous; rather there may be some time lags. Therefore as a part of the future work the model considered here can be refined to incorporate time delays in the system to make it more realistic. Also, there are many components in this model that may be regarded as stochastic rather than deterministic, and these variations may significantly alter the dynamics of the system. Therefore, as our future work we can incorporate stochastic differential equations in modelling and study its dynamics. Controlling the spread of tumor cells is now a challenging and important issue to study. Chemotherapy, immunotherapy, radiation therapy, surgery, and so forth are most useful therapies to control and reduce the spread of tumor cells. So, the development of these therapies and identification of the most effective therapy against the spread of tumor cells are the primary goal of health administrators, policymakers, and researchers. Our model study is a small step towards the goal by which we want to identify the parameters of interest for further study.
Acknowledgments
The authors are grateful to the anonymous referees and the editor (Professor Giovanni P. Galdi) for their careful reading, valuable comments, and helpful suggestions, which have helped them to improve the presentation of this work significantly.
References
 V. A. Kuznetsov and G. D. Knott, “Modeling tumor regrowth and immunotherapy,” Mathematical and Computer Modelling, vol. 33, no. 1213, pp. 1275–1287, 2001. View at: Publisher Site  Google Scholar
 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. View at: Google Scholar
 D. Kirschner and J. C. Panetta, “Modeling immunotherapy of the tumor—immune interaction,” Journal of Mathematical Biology, vol. 37, no. 3, pp. 235–252, 1998. View at: Google Scholar
 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 Site  Google Scholar
 L. G. De Pillis, W. Gu, and A. E. Radunskaya, “Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations,” Journal of Theoretical Biology, vol. 238, no. 4, pp. 841–862, 2006. View at: Publisher Site  Google Scholar
 L. G. De Pillis and A. Radunskaya, “A mathematical tumor model with immune resistance and drug therapy: an optimal control approach,” Journal of Theoretical Medicine, vol. 3, no. 2, pp. 79–100, 2001. View at: Google Scholar
 J. C. Arciero, T. L. Jackson, and D. E. Kirschner, “A mathematical model of tumorimmune evasion and siRNA treatment,” Discrete and Continuous Dynamical Systems B, vol. 4, no. 1, pp. 39–58, 2004. View at: Google Scholar
 N. Bellomo, A. Bellouquid, and M. Delitala, “Mathematical topics on the modelling complex multicellular systems and tumor immune cells competition,” Mathematical Models and Methods in Applied Sciences, vol. 14, no. 11, pp. 1683–1733, 2004. View at: Publisher Site  Google Scholar
 N. Bellomo and L. Preziosi, “Modelling and mathematical problems related to tumor evolution and its interaction with the immune system,” Mathematical and Computer Modelling, vol. 32, no. 34, pp. 413–452, 2000. View at: Publisher Site  Google Scholar
 B. S. Chan and P. Yu, “Bifurcation analysis in a model of cytotoxic Tlymphocyte response to viral infections,” Nonlinear Analysis: Real World Applications, vol. 13, no. 1, pp. 64–77, 2012. View at: Publisher Site  Google Scholar
 L. G. De Pillis, A. E. Radunskaya, and C. L. Wiseman, “A validated mathematical model of cellmediated immune response to tumor growth,” Cancer Research, vol. 65, no. 17, pp. 7950–7958, 2005. View at: Publisher Site  Google Scholar
 L. Derbel, “Analysis of a new model for tumorimmune system competition including longtime scale effects,” Mathematical Models and Methods in Applied Sciences, vol. 14, no. 11, pp. 1657–1681, 2004. View at: Publisher Site  Google Scholar
 A. D'Onofrio, “A general framework for modeling tumorimmune system competition and immunotherapy: mathematical analysis and biomedical inferences,” Physica D, vol. 208, no. 34, pp. 220–235, 2005. View at: Publisher Site  Google Scholar
 F. Nani and H. I. Freedman, “A mathematical model of cancer treatment by immunotherapy,” Mathematical Biosciences, vol. 163, no. 2, pp. 159–199, 2000. View at: Publisher Site  Google Scholar
 S. T. R. Pinho, F. S. Bacelar, R. F. S. Andrade, and H. I. Freedman, “A mathematical model for the effect of antiangiogenic therapy in the treatment of cancer tumors by chemotherapy,” Nonlinear Analysis: Real World Applications, vol. 14, pp. 815–828, 2013. View at: Google Scholar
 H. Siu, E. S. Vivetta, R. D. May, and J. W. Uhr, “Tumor dormancy—I. Regression of BCL1 tumor and induction of a dormant tumor state in mice chimeric at the major histocompatibility complex,” Journal of Immunology, vol. 137, no. 4, pp. 1376–1382, 1986. View at: Google Scholar
 T. Takayanagi and A. Ohuchi, “A mathematical analysis of the interactions between immunogenic tumor cells and cytotoxic T lymphocytes,” Microbiology and Immunology, vol. 45, no. 10, pp. 709–715, 2001. View at: Google Scholar
 R. Yafia, “Dynamics analysis and limit cycle in a delayed model for tumor growth with quiescene,” Nonlinear Analysis. Modelling and Control, vol. 11, pp. 95–110, 2006. View at: Google Scholar
 R. Martin and K. L. Teo, Optimal Control of Drug Administration in Cancer Chemotherapy, World Scientific, River Edge, NJ, USA, 1994.
 A. S. Matveev and A. V. Savkin, “Application of optimal control theory to analysis of cancer chemotherapy regimens,” Systems and Control Letters, vol. 46, no. 5, pp. 311–321, 2002. View at: Publisher Site  Google Scholar
 L. G. de Pillis, W. Gu, K. R. Fister et al., “Chemotherapy for tumors: an analysis of the dynamics and a study of quadratic and linear optimal controls,” Mathematical Biosciences, vol. 209, no. 1, pp. 292–315, 2007. View at: Publisher Site  Google Scholar
 L. G. De Pillis and A. Radunskaya, “The dynamics of an optimally controlled tumor model: a case study,” Mathematical and Computer Modelling, vol. 37, no. 11, pp. 1221–1244, 2003. View at: Publisher Site  Google Scholar
 M. Engelhart, D. Lebiedz, and S. Sager, “Optimal control for selected cancer chemotherapy ODE models: a view on the potential of optimal schedules and choice of objective function,” Mathematical Biosciences, vol. 229, no. 1, pp. 123–134, 2011. View at: Publisher Site  Google Scholar
 K. R. Fister and J. Donnelly, “Immunotherapy: an optimal control theory approach,” Mathematical Biosciences and Engineering, vol. 2, no. 3, p. 499, 2005. View at: Google Scholar
 K. R. Fister and J. C. Panetta, “Optimal control applied to cellcyclespecific cancer chemotherapy,” SIAM Journal on Applied Mathematics, vol. 60, no. 3, pp. 1059–1072, 2000. View at: Google Scholar
 K. R. Fister and J. C. Panetta, “Optimal control applied to competing chemotherapeutic cellkill strategies,” SIAM Journal on Applied Mathematics, vol. 63, no. 6, pp. 1954–1971, 2003. View at: Publisher Site  Google Scholar
 U. Ledzewicz and H. Schättler, “Antiangiogenic therapy in cancer treatment as an optimal control problem,” SIAM Journal on Control and Optimization, vol. 46, no. 3, pp. 1052–1079, 2007. View at: Publisher Site  Google Scholar
 S. M. Mamat and A. Kartono, “Mathematical model of cancer treatment using immunotherapy, chemotherapy and biochemotherapy,” Applied Mathematical Sciences, vol. 7, no. 5, pp. 247–261, 2013. View at: Google Scholar
 H. R. Joshi, “Optimal control of an HIV immunology model,” Optimal Control Applications and Methods, vol. 23, no. 4, pp. 199–213, 2002. View at: Publisher Site  Google Scholar
 G. Zaman, Y. Han Kang, and I. H. Jung, “Stability analysis and optimal vaccination of an SIR epidemic model,” BioSystems, vol. 93, no. 3, pp. 240–249, 2008. View at: Publisher Site  Google Scholar
 J. K. Hale, Theory of Functional Differential Equations, Springer, New York, Ny, USA, 1977.
 M. Kot, Elements of Mathematical Ecology, Cambridge University Press, Cambridge, UK, 2001.
 L. Bannock, http://www.doctorbannock.com/nutrition.html.
 P. Calabresi and P. S. Schein, Eds., Medical Oncology: Basic Principles and Clinical Management of Cancer, McGrawHill, New York, NY, USA, 2nd edition, 1993.
 A. Diefenbach, E. R. Jensen, A. M. Jamieson, and D. H. Raulet, “Rae1 and H60 ligands of the NKG2D receptor stimulate tumour immunity,” Nature, vol. 413, no. 6852, pp. 165–171, 2001. View at: Publisher Site  Google Scholar
 M. C. Perry, Ed., The Chemotherapy Source Book, Lippincott Williams and Wilkins, 3rd edition, 2001.
 K. Blayneh, Y. Cao, and H.D. Kwon, “Optimal control of vectorborne diseases: treatment and prevention,” Discrete and Continuous Dynamical Systems, vol. 11, no. 3, pp. 587–611, 2009. View at: Publisher Site  Google Scholar
 S. Lcnhart and J. T. Workman, Optimal Control Applied to Biological Mathods, Chapman and Hall/CRC, London, UK, 2007.
 G. W. Swan, Applications of Optimal Control Theory in Biomedicine, Marcel Dekker, New York, NY, USA, 1984.
 J. M. Tchuenche, S. A. Khamis, F. B. Agusto, and S. C. Mpeshe, “Optimal control and sensitivity analysis of an influenza model with treatment and vaccination,” Acta Biotheoretica, vol. 59, no. 1, pp. 1–28, 2011. View at: Publisher Site  Google Scholar
 W. H. Fleming and R. W. Rishel, Deterministic and Stochastic Optimal Control, Springer, New York, NY, USA, 1975.
 D. L. Lukes, Differential Equations: Classical to Controlled, Mathematics in Science and Engineering, Academic Press, New York, NY, USA, 1982.
 L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze, and E. F. Mishchenko, The Mathematical Theory of Optimal Process, Gordon and Breach, 1962.
Copyright
Copyright © 2013 Swarnali Sharma and G. P. Samanta. 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.