
Citation: Taeyong Lee, Adrianne L. Jenner, Peter S. Kim, Jeehyun Lee. Application of control theory in a delayed-infection and immune-evading oncolytic virotherapy[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2361-2383. doi: 10.3934/mbe.2020126
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The construction of discrete fractional sums and differences from the knowledge of samples of their corresponding continuous integrals and derivatives arises in the context of discrete fractional calculus; see [1,2,3,4,5,6] for more details. Recently, discrete fractional operators with more general forms of their kernels and properties have gathered attention in both areas of physics and mathematics; see [7,8,9,10].
In discrete fractional calculus theory, we say that F is monotonically increasing at a time step t if the nabla of F is non-negative, i.e., (∇F)(t):=F(t)−F(t−1)≥0 for each t in the time scale set Nc0+1:={c0+1,c0+2,…}. Moreover, the function F is θ−monotonically increasing (or decreasing) on Nc0 if F(t+1)>θF(t)(orF(t+1)<θF(t)) for each t∈Na). In [11,12] the authors considered 1−monotonicity analysis for standard discrete Riemann-Liouville fractional differences defined on N0 and in [13] the authors generalized the above by introducing θ−monotonicity increasing and decreasing functions and then obtained some θ−monotonicity analysis results for discrete Riemann-Liouville fractional differences defined on N0. In [14,15,16], the authors considered monotonicity and positivity analysis for discrete Caputo, Caputo-Fabrizio and Attangana-Baleanu fractional differences and in [17,18] the authors considered monotonicity and positivity results for abstract convolution equations that could be specialized to yield new insights into qualitative properties of fractional difference operators. In [19], the authors presented positivity and monotonicity results for discrete Caputo-Fabrizo fractional operators which cover both the sequential and non-sequential cases, and showed both similarities and dissimilarities between the exponential kernel case (that is included in Caputo-Fabrizo fractional operators) and fractional differences with other types of kernels. Also in [20] the authors extended the results in [19] to discrete Attangana-Baleanu fractional differences with Mittag-Leffler kernels. The main theoretical developments of monotonicity and positivity analysis in discrete fractional calculus can be found in [21,22,23,24] for nabla differences, and in [25,26,27,28] for delta differences.
The main idea in this article is to analyse discrete Caputo-Fabrizo fractional differences with exponential kernels in the Riemann-Liouville sense. The results are based on a notable lemma combined with summation techniques. The purpose of this article is two-fold. First we show the positiveness of discrete fractional operators from a theoretical point of view. Second we shall complement the theoretical results numerically and graphically based on the standard plots and heat map plots.
The plan of the article is as follows. In Section 2 we present discrete fractional operators and the main lemma. Section 3 analyses the discrete fractional operator in a theoretical sense. In Section 4 we discuss our theoretical strategy on standard plots (Subsection 4.1) and heat map plots (Subsection 4.2). Finally, in Section 5 we summarize our findings.
First we recall the definitions in discrete fractional calculus; see [2,3,5] for more information.
Definition 2.1. (see [2,Definition 2.24]). Let c0∈R, 0<θ≤1, F be defined on Nc0 and Λ(θ)>0 be a normalization constant. Then the following operator
(CFRc0∇θF)(t):=Λ(θ)∇tt∑r=c0+1F(r)(1−θ)t−r{t∈Nc0+1}, |
is called the discrete Caputo-Fabrizio fractional operator with exponential kernels in the Riemann-Liouville sense CFR, and the following operator
(CFCc0∇θF)(t):=Λ(θ)t∑r=c0+1(∇rF)(r)(1−θ)t−r{t∈Nc0+1}, |
is called the discrete Caputo-Fabrizo fractional operator with exponential kernels in the Caputo sense CFC.
Definition 2.2 (see [3]). For F:Nc0−κ→R with κ<θ≤κ+1 and κ∈N0, the discrete nabla CFC and CFR fractional differences can be expressed as follows:
(CFCc0∇θF)(t)=(CFCc0∇θ−κ∇κF)(t), |
and
(CFRc0∇θF)(t)=(CFRc0∇θ−κ∇κF)(t), |
respectively, for each t∈Nc0+1.
The following lemma is essential later.
Lemma 2.1. Assume that F is defined on Nc0 and 1<θ<2. Then the CFR fractional difference is
(CFRc0∇θF)(t)=Λ(θ−1){(∇F)(t)+(1−θ)(2−θ)t−c0−2(∇F)(c0+1)+(1−θ)t−1∑r=c0+2(∇rF)(r)(2−θ)t−r−1}, |
for each t∈Nc0+2.
Proof. From Definitions 2.1 and 2.2, the following can be deduced for 1<θ<2:
(CFRc0∇θF)(t)=Λ(θ−1){t∑r=c0+1(∇rF)(r)(2−θ)t−r−t−1∑r=c0+1(∇rF)(r)(2−θ)t−r−1}=Λ(θ−1){(∇F)(t)+t−1∑r=c0+1(∇rF)(r)[(2−θ)t−r−(2−θ)t−r−1]}=Λ(θ−1){(∇F)(t)+(1−θ)(2−θ)t−c0−2(∇F)(c0+1)+(1−θ)t−1∑r=c0+2(∇rF)(r)(2−θ)t−r−1}, |
for each t∈Nc0+2.
In the following theorem, we will show that F is monotonically increasing at two time steps even if (CFRc0∇θF)(t) is negative at the two time steps.
Theorem 3.1. Let the function F be defined on Nc0+1, and let 1<θ<2 and ϵ>0. Assume that
(CFRc0∇θF)(t)>−ϵΛ(θ−1)(∇F)(c0+1)fort∈{c0+2,c0+3}s.t.(∇F)(c0+1)≥0. | (3.1) |
If (1−θ)(2−θ)<−ϵ, then (∇F)(c0+2) and (∇F)(c0+3) are both nonnegative.
Proof. From Lemma 2.1 and condition (3.1) we have
(∇F)(t)≥−(∇F)(c0+1)[(1−θ)(2−θ)t−c0−2+ϵ]−(1−θ)t−1∑r=c0+2(∇rF)(r)(2−θ)t−r−1, | (3.2) |
for each t∈Nc0+2. At t=c0+2, we have
(∇F)(c0+2)≥−(∇F)(c0+1)[(1−θ)+ϵ]−(1−θ)c0+1∑r=c0+2(∇rF)(r)(2−θ)c0+1−r⏟=0≥0, |
where we have used (1−θ)<(1−θ)(2−θ)<−ϵ and (∇F)(c0+1)≥0 by assumption. At t=c0+3, it follows from (3.2) that
(∇F)(c0+3)=−(∇F)(c0+1)[(1−θ)(2−θ)+ϵ]−(1−θ)c0+2∑r=c0+2(∇rF)(r)(2−θ)c0+2−r=−(∇F)(c0+1)⏟≥0[(1−θ)(2−θ)+ϵ]⏟<0−(1−θ)⏟<0(∇F)(c0+2)⏟≥0≥0, | (3.3) |
as required. Hence the proof is completed.
Remark 3.1. It worth mentioning that Figure 1 shows the graph of θ↦(1−θ)(2−θ) for θ∈(1,2).
In order for Theorem 3.1 to be applicable, the allowable range of ϵ is ϵ∈(0,−(2−θ)(1−θ)) for a fixed θ∈(1,2)
Now, we can define the set Hκ,ϵ as follows
Hκ,ϵ:={θ∈(1,2):(1−θ)(2−θ)κ−c0−2<−ϵ}⊆(1,2),∀κ∈Nc0+3. |
The following lemma shows that the collection {Hκ,ϵ}∞κ=c0+1 forms a nested collection of decreasing sets for each ϵ>0.
Lemma 3.1. Let 1<θ<2. Then, for each ϵ>0 and κ∈Nc0+3 we have that Hκ+1,ϵ⊆Hκ,ϵ.
Proof. Let θ∈Hκ+1,ϵ for some fixed but arbitrary κ∈Nc0+3 and ϵ>0. Then we have
(1−θ)(2−θ)κ−c0−1=(1−θ)(2−θ)(2−θ)κ−c0−2<−ϵ. |
Considering 1<θ<2 and κ∈Nc0+3, we have 0<2−θ<1. Consequently, we have
(1−θ)(2−θ)κ−c0−2<−ϵ⋅ 12−θ⏟>1<−ϵ. |
This implies that θ∈Hκ,ϵ, and thus Hκ+1,ϵ⊆Hκ,ϵ.
Now, Theorem 3.1 and Lemma 3.1 lead to the following corollary.
Corollary 3.1. Let F be a function defined on Nc0+1, θ∈(1,2) and
(CFRc0∇θF)(t)>−ϵΛ(θ−1)(∇F)(c0+1)suchthat(∇F)(c0+1)≥0, | (3.4) |
for each t∈Nsc0+3:={c0+3,c0+4,…,s} and some s∈Nc0+3. If θ∈Hs,ϵ, then we have (∇F)(t)≥0 for each t∈Nsc0+1.
Proof. From the assumption θ∈Hs,ϵ and Lemma 3.1, we have
θ∈Hs,ϵ=Hs,ϵ∩s−1⋂κ=c0+3Hκ,ϵ. |
This leads to
(1−θ)(2−θ)t−c0−2<−ϵ, | (3.5) |
for each t∈Nsc0+3.
Now we use the induction process. First for t=c0+3 we obtain (∇F)(c0+3)≥0 directly as in Theorem 3.1 by considering inequalities (Eq 3.4) and (Eq 3.5) together with the given assumption (∇F)(c0+1)≥0. As a result, we can inductively iterate inequality (Eq 3.2) to get
(∇F)(t)≥0, |
for each t∈Nsc0+2. Moreover, (∇F)(c0+1)≥0 by assumption. Thus, (∇F)(t)≥0 for each t∈Nsc0+1 as desired.
In this section, we consider the methodology for the positivity of ∇F based on previous observations in Theorem 3.1 and Corollary 3.1 in such a way that the initial conditions are known. Later, we will illustrate other parts of our article via standard plots and heat maps for different values of θ and ϵ. The computations in this section were performed with MATLAB software.
Example 4.1. Considering Lemma 1 with t:=c0+3:
(CFRc0∇θF)(c0+3)=Λ(θ−1){(∇F)(c0+3)+(1−θ)(2−θ)(∇F)(c0+1)+(1−θ)c0+2∑r=c0+2(∇rF)(r)(2−θ)c0+2−r}. |
For c0=0, it follows that
(CFR0∇θF)(3)=Λ(θ−1){(∇F)(3)+(1−θ)(2−θ)(∇F)(1)+(1−θ)2∑r=2(∇rF)(r)(2−θ)2−r}=Λ(θ−1){(∇F)(3)+(1−θ)(2−θ)(∇F)(1)+(1−θ)(∇F)(2)}=Λ(θ−1){F(3)−F(2)+(1−θ)(2−θ)[F(1)−F(0)]+(1−θ)[F(2)−F(1)]}. |
If we take θ=1.99,F(0)=0,F(1)=1,F(2)=1.001,F(3)=1.005, and ϵ=0.007, we have
(CFR0∇1.99F)(3)=Λ(0.99){0.004+(−0.99)(0.01)(0)+(−0.99)(0.001)}=−0.0069Λ(0.99)>−0.007Λ(0.99)=−ϵΛ(0.99)(∇F)(1). |
In addition, we see that (1−θ)(2−θ)=−0.0099<−0.007=−ϵ. Since the required conditions are satisfied, Theorem 3.1 ensures that (∇F)(3)>0.
In Figure 2, the sets Hκ,0.008 and Hκ,0.004 are shown for different values of κ, respectively in Figure 2a, b. It is noted that Hκ,0.008 and Hκ,0.004 decrease by increasing the values of κ. Moreover, in Figure 2a, the set Hκ,0.008 becomes empty for κ≥45; however, in Figure 2b, we observe the non-emptiness of the set Hκ,0.004 for many larger values of κ up to 90. We think that the measures of Hκ,0.008 and Hκ,0.004 are not symmetrically distributed when κ increases (see Figure 2a, b). We do not have a good conceptual explanation for why this symmetric behavior is observed. In fact, it is not clear why the discrete nabla fractional difference CFRc0∇θ seems to give monotonically when θ→1 rather than for θ→2, specifically, it gives a maximal information when θ is very close to 1 as ϵ→0+.
In the next figure (Figure 3), we have chosen a smaller ϵ (ϵ=0.001), we see that the set Hκ,0.001 is non-empty for κ>320. This tells us that small choices in ϵ give us a more widely applicable result.
In this part, we introduce the set Hκ:={κ:θ∈Hκ,ϵ} to simulate our main theoretical findings for the cardinality of the set Hκ via heat maps in Figure 4a–d. In these figures: we mean the warm colors such as red ones and the cool colors such as blue ones. Moreover, the θ values are on the x−axis and ϵ values are on the y−axis. We choose ϵ in the interval [0.00001, 0.0001]. Then, the conclusion of these figures are as follows:
● In Figure 4a when θ∈(1,2) and Figure 4b when θ∈(1,1.5), we observe that the warmer colors are somewhat skewed toward θ very close to 1, and the cooler colors cover the rest of the figures for θ above 1.05.
● In Figure 4c, d, the warmest colors move strongly towards the lower values of θ, especially, when θ∈(1,1.05). Furthermore, when as θ increases to up to 1.0368, it drops sharply from magenta to cyan, which implies a sharp decrease in the cardinality of Hκ for a small values of ϵ as in the interval [0.00001, 0.0001].
On the other hand, for larger values of ϵ, the set Hκ will tend to be empty even if we select a smaller θ in such an interval (1,1.05). See the following Figure 5a, b for more.
In conclusion, from Figures 4 and 5, we see that: For a smaller value of ϵ, the set Hκ,ϵ tends to remain non-empty (see Figure 4), unlike for a larger value of ϵ (see Figure 5). Furthermore, these verify that Corollary 3.1 will be more applicable for 1<θ<1.05 and 0.01<ϵ<0.1 as shown in Figure 4d.
Although, our numerical data strongly note the sensitivity of the set Hκ when slight increasing in ϵ is observed for θ close to 2 compares with θ close to 1.
In this paper we developed a positivity method for analysing discrete fractional operators of Riemann-Liouville type based on exponential kernels. In our work we have found that (∇F)(3)≥0 when (CFRc0∇θF)(t)>−ϵΛ(θ−1)(∇F)(c0+1) such that (∇F)(c0+1)≥0 and ϵ>0. We continue to extend this result for each value of t in Nsc0+1 as we have done in Corollary 3.1.
In addition we presented standard plots and heat map plots for the discrete problem that is solved numerically. Two of the graphs are standard plots for Hκ,ϵ for different values of κ and ϵ (see Figure 2), and the other six graphs consider the cardinality of Hκ for different values of ϵ and θ (see Figures 4 and 5). These graphs ensure the validity of our theoretical results.
In the future we hope to apply our method to other types of discrete fractional operators which include Mittag-Leffler and their extensions in kernels; see for example [5,6].
This work was supported by the Taif University Researchers Supporting Project Number (TURSP-2020/86), Taif University, Taif, Saudi Arabia.
The authors declare there is no conflict of interest.
[1] | L.-Q. Fu, S. Wang, M.-H. Cai, X.-J. Wang, J.-Y. Chen, X.-M. Tong, et al., Recent advances in oncolytic virus-based cancer therapy, Virus Res., (2019), 197675. |
[2] | I. R. Eissa, I. Bustos-Villalobos, T. Ichinose, S. Matsumura, Y. Naoe, N. Miyajima, et al., The current status and future prospects of oncolytic viruses in clinical trials against melanoma, glioma, pancreatic, and breast cancers, Cancers, 10 (2018), 356. |
[3] | J. Raja, J. M. Ludwig, S. N. Gettinger, K. A. Schalper, H. S. Kim. Oncolytic virus immunotherapy: future prospects for oncology, J. Immunother. Cancer, 6 (2018), 140. |
[4] | R. Yokoda, B. M. Nagalo, B. Vernon, R. Oklu, H. Albadawi, T. T. DeLeon, et al., Oncolytic virus delivery: from nano-pharmacodynamics to enhanced oncolytic effect, Oncolytic Virotherapy, 6 (2017), 39. |
[5] | J. W. Choi, E. Kang, O. J. Kwon, T. J. Yun, H. K. Park, P. H. Kim, et al., Local sustained delivery of oncolytic adenovirus with injectable alginate gel for cancer virotherapy, Gene Ther., 20 (2013), 880. |
[6] | M. A Croyle, N. Chirmule, Y. Zhang, J. M. Wilson. "stealth" adenoviruses blunt cell-mediated and humoral immune responses against the virus and allow for significant gene expression upon readministration in the lung, J. Virol., 75 (2001), 4792-4801. |
[7] | B.-K. Jung, E. Oh, J. Hong, Y. Lee, K. D. Park, C.-O. Yun, A hydrogel matrix prolongs persistence and promotes specific localization of an oncolytic adenovirus in a tumor by restricting nonspecific shedding and an antiviral immune response, Biomater., 147 (2017), 26-38. |
[8] | S.-H. Jung, J.-W. Choi, C.-O. Yun, J. Y. Yhee, R. Price, S. H. Kim, et al., Sustained local delivery of oncolytic short hairpin rna adenoviruses for treatment of head and neck cancer, J Gene Med., 16 (2014), 143-152. |
[9] | E. Oh, J. Oh, J. Hong, Y. Chung, Y. Lee, K. Park, et al., Optimized biodegradable polymeric reservoir-mediated local and sustained co-delivery of dendritic cells and oncolytic adenovirus coexpressing IL-12 and GM-CSF for cancer immunotherapy, J. Control. Release, 259 (2017), 115-127. |
[10] | J. P. Smith, S. Kanekal, M. B. Patawaran, J. Y. Chen, R. E. Jones, E. K. Orenberg, et al., Drug retention and distribution after intratumoral chemotherapy with fluorouracil/epinephrine injectable gel in human pancreatic cancer xenografts, Cancer Chemoth. Pharm., 44 (1999), 267-274. |
[11] | S. J. Wade, A. Zuzic, J. Foroughi, S. Talebian, M. Aghmesheh, S. E. Moulton, et al., Preparation and in vitro assessment of wet-spun gemcitabine-loaded polymeric fibers: Towards localized drug delivery for the treatment of pancreatic cancer, Pancreatology, 17 (2017), 795-804. |
[12] | M. Riley, W. Vermerris, Recent advances in nanomaterials for gene delivery-a review, Nanomaterials, 7 (2017), 94. |
[13] | J. W. Choi, S. J. Jung, D. Kasala, J. K. Hwang, J. Hu, Y. H. Bae, et al., ph-sensitive oncolytic adenovirus hybrid targeting acidic tumor microenvironment and angiogenesis, J. Control. Release, 205 (2015), 134-143. |
[14] | S. Tseng, I. M. Kempson, K. Huang, H. Li, Y. Fa, Y. Ho, et al., Targeting tumor microenvironment by bioreduction-activated nanoparticles for light-triggered virotherapy, ACS Nano, 12 (2018), 9894-9902. |
[15] | A. L. Jenner, F. Frascoli, A. C. F. Coster, P. S. Kim, Enhancing oncolytic virotherapy: Observations from a voronoi cell-based model, J. Theor. Biol., (2019), 110052. |
[16] | A. L. Jenner, P. S. Kim, F. Frascoli, Oncolytic virotherapy for tumours following a gompertz growth law, J. Theor. Biol., 480 (2019), 129-140. |
[17] | J. Malinzi, R. Ouifki, A. Eladdadi, D. Torres, K. A. White, Enhancement of chemotherapy using oncolytic virotherapy: mathematical and optimal control analysis, arXiv preprint arXiv:1807.04329, 2018. |
[18] | E. Ratajczyk, U. Ledzewicz, H. Schättler, Optimal control for a mathematical model of glioma treatment with oncolytic therapy and tnf-α inhibitors, J. Optimiz. Theory App., 176 (2018), 456-477. |
[19] | M. I. Titze, J. Frank, M. Ehrhardt, S. Smola, N. Graf, T. Lehr, A generic viral dynamic model to systematically characterize the interaction between oncolytic virus kinetics and tumor growth, Eur. J. Pharm. Sci., 97 (2017), 38-46. |
[20] | T. Cassidy, M. Craig, Determinants of combination gm-csf immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization, PLoS Comput. Biol., 15 (2019), e1007495. |
[21] | A. Friedman, X. Lai, Combination therapy for cancer with oncolytic virus and checkpoint inhibitor: A mathematical model, PloS One, 13 (2018), e0192449. |
[22] | W. Mok, T. Stylianopoulos, Y. Boucher, R. K. Jain, Mathematical modeling of herpes simplex virus distribution in solid tumors: implications for cancer gene therapy, Clin. Cancer Res., 15 (2009), 2352-2360. |
[23] | L. R. Paiva, C. Binny, S. C. Ferreira, M. L. Martins, A multiscale mathematical model for oncolytic virotherapy, Cancer Res., 69 (2009), 1205-1211. |
[24] | L. G. De Pillis, A. Radunskaya, A mathematical tumor model with immune resistance and drug therapy: an optimal control approach, Comput. Math. Methods Med., 3 (2001), 79-100. |
[25] | R. Zurakowski, D. Wodarz, Model-driven approaches for in vitro combination therapy using onyx-015 replicating oncolytic adenovirus, J. Theor. Biol., 245 (2007), 1-8. |
[26] | G. Khan, W. Ahmed, P. S. Philip, M. H. Ali, A. Adem, Healthy rabbits are susceptible to EpsteinBarr virus infection and infected cells proliferate in immunosuppressed animals, Virol. J., 12 (2015), 28. |
[27] | Z. Z. Wang, Z. M. Guo, H. Smith, A mathematical model of oncolytic virotherapy with time delay, Math. Biosci. Eng., 16 (2019), 1836-1860. |
[28] | E. Basner-Tschakarjan, F. Mingozzi, Cell-mediated immunity to AAV vectors, evolving concepts and potential solutions, Front. Immun., 5 (2014), 350. |
[29] | R. H. Byrd, M. E. Hribar, J. Nocedal, An interior point algorithm for large-scale nonlinear programming, SIAM J. Optim., 9 (1999), 877-900. |
[30] | A. L. Jenner, C.-O. Yun, P. S. Kim, A. C. F. Coster, Mathematical modelling of the interaction between cancer cells and an oncolytic virus: insights into the effects of treatment protocols, Bull. Math. Biol., 80 (2018), 1615-1629. |
[31] | P. Kim, J. Sohn, J. Choi, Y. Jung, S. Kim, S. Haam, et al., Active targeting and safety profile of peg-modified adenovirus conjugated with herceptin, Biomater., 32 (2011), 2314-2326. |
[32] | K. Murphy, C. Weaver, Janeway's immunobiology, Garland Science, 2016. |