December  2017, 22(10): 3771-3782. doi: 10.3934/dcdsb.2017189

Tumor growth dynamics with nutrient limitation and cell proliferation time delay

1. 

Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom

2. 

Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia

3. 

Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

4. 

School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA

Received  October 2016 Revised  May 2017 Published  July 2017

It is known that avascular spherical solid tumors grow monotonically, often tends to a limiting final size. This is repeatedly confirmed by various mathematical models consisting of mostly ordinary differential equations. However, cell growth is limited by nutrient and its proliferation incurs a time delay. In this paper, we formulate a nutrient limited compartmental model of avascular spherical solid tumor growth with cell proliferation time delay and study its limiting dynamics. The nutrient is assumed to enter the tumor proportional to its surface area. This model is a modification of a recent model which is built on a two-compartment model of cancer cell growth with transitions between proliferating and quiescent cells. Due to the limitation of resources, it is imperative that the population values or densities of a population model be nonnegative and bounded without any technical conditions. We confirm that our model meets this basic requirement. From an explicit expression of the tumor final size we show that the ratio of proliferating cells to the total tumor cells tends to zero as the death rate of quiescent cells tends to zero. We also study the stability of the tumor at steady states even though there is no Jacobian at the trivial steady state. The characteristic equation at the positive steady state is complicated so we made an initial effort to study some special cases in details. We find that delay may not destabilize the positive steady state in a very extreme situation. However, in a more general case, we show that sufficiently long cell proliferation delay can produce oscillatory solutions.

Citation: Ahuod Alsheri, Ebraheem O. Alzahrani, Asim Asiri, Mohamed M. El-Dessoky, Yang Kuang. Tumor growth dynamics with nutrient limitation and cell proliferation time delay. Discrete & Continuous Dynamical Systems - B, 2017, 22 (10) : 3771-3782. doi: 10.3934/dcdsb.2017189
References:
[1]

J. A. Adam and B. Bellomo (ed), A Survey of Models on Tumour Immune Systems Dynamics, Birkhüauser, 1997.Google Scholar

[2]

E. O. AlzahraniA. AsiriM. M. El-Dessoky and Y. Kuang, Quiescence as an explanation of Gompertzian tumor growth revisited, Mathematical Biosciences, 254 (2014), 76-82. doi: 10.1016/j.mbs.2014.06.009. Google Scholar

[3]

E. O. Alzahrani and Y. Kuang, Nutrient limitations as an explanation of Gompertzian tumor growth, Discrete Cont. Dyn. Syst.-B., 21 (2016), 357-372. doi: 10.3934/dcdsb.2016.21.357. Google Scholar

[4]

R. P. Araujo and D. L. S. McElwain, A history of the study of solid tumour growth: The contribution of mathematical modelling, Bull. Math. Biol., 66 (2004), 1039-1091. doi: 10.1016/j.bulm.2003.11.002. Google Scholar

[5]

L. von Bertalanffy, Quantitative laws in metabolism and growth, Quart. Rev. Biol., 32 (1957), 217-231. doi: 10.1086/401873. Google Scholar

[6]

H. M. ByrneJ. R. KingD. L. S. McElwain and L. Preziosi, A two-phase model of solid tumour growth, Applied Mathematics Letters, 16 (2003), 567-573. doi: 10.1016/S0893-9659(03)00038-7. Google Scholar

[7]

S. E. Eikenberry, J. D. Nagy and Y. Kuang, The evolutionary impact of androgen levels on prostate cancer in a multi-scale mathematical model Biology Direct, 5 (2010), p24. doi: 10.1186/1745-6150-5-24. Google Scholar

[8]

S. E. Eikenberry, C. Thalhauser and Y. Kuang, Tumor-immune interaction, surgical treatment and cancer recurrence in a mathematical model of melanoma PLoS Comput. Biol. , 5 (2009), e1000362, 18pp. doi: 10.1371/journal.pcbi.1000362. Google Scholar

[9]

R. A. EverettY. ZhaoK. B. Flores and Y. Kuang, Data and implication based comparison of two chronic myeloid leukemia models, Math. Biosc. Eng., 10 (2013), 1501-1518. doi: 10.3934/mbe.2013.10.1501. Google Scholar

[10]

C. L. Frenzen and J. D. Murray, A cell kinetics justification for Gompertz equation, SIAM J. Appl. Math., 46 (1986), 614-629. doi: 10.1137/0146042. Google Scholar

[11]

S. Geritz and M. Gyllenberg, A mechanistic derivation of the DeAngelis-Beddington functional response, Journal of Theoretical Biology, 314 (2012), 106-108. doi: 10.1016/j.jtbi.2012.08.030. Google Scholar

[12]

B. Gompertz, On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies, Phil. Trans. Royal Soc. London, 115 (1825), 513-583. Google Scholar

[13]

M. Gyllenberg and G. F. Webb, Quiescence as an explanation of Gompertzian tumor growth, Growth Dev Aging, 53 (1989), 25-33. Google Scholar

[14]

M. Gyllenberg and G. F. Webb, A nonlinear structured population model of tumor growth with quiescence, J. Math. Biol., 28 (1990), 671-694. doi: 10.1007/BF00160231. Google Scholar

[15]

F. Kozusko and Z. Bajzer, Combining Gompertzian growth and cell population dynamics, Math. Biosc., 185 (2003), 153-167. doi: 10.1016/S0025-5564(03)00094-4. Google Scholar

[16]

Y. Kuang, Delay Differential Equations: With Applications in Population Dynamics, Academic Press, 1993. Google Scholar

[17]

Y. Kuang, J. D. Nagy and S. E. Eikenberry Introduction to Mathematical Oncology, CRC Press, 2016. Google Scholar

[18]

A. O. Martinez and R. J. Griego, Growth dynamics of multicell spheroids from three murine tumors, Growth, 44 (1980), 112-122. Google Scholar

[19]

M. Marusic and S. Vuk-Pavlovic, Prediction power of mathematical models for tumor growth, Journal of Biological Systems, 1 (1993), 69-78. Google Scholar

[20]

L. NortonR. SimonH. D. Brereton and A. E. Bogden, Predicting the course of Gompertzian growth, Nature, 264 (1976), 542-545. doi: 10.1038/264542a0. Google Scholar

[21]

T. Portz, Y. Kuang and J. D. Nagy, A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy AIP Advances, 2 (2012), 011002. doi: 10.1063/1.3697848. Google Scholar

[22]

J. A. Sherratt and M. J. Chaplain, A new mathematical model for avascular tumor growth, J. Math. Biol., 43 (2001), 291-312. doi: 10.1007/s002850100088. Google Scholar

[23]

C. J. ThalhauserT. SankarM. C. Preul and Y. Kuang, Explicit separation of growth and motility in a new tumor cord model, Bulletin of Math. Biol., 71 (2009), 585-601. doi: 10.1007/s11538-008-9372-8. Google Scholar

[24]

D. Wallace and X. Guo, Properties of tumor spheroid growth exhibited by simple mathematical models, Frontiers in Oncology, 3 (2013), 1-9. doi: 10.3389/fonc.2013.00051. Google Scholar

[25]

R. Yafia, Dynamics analysis and limit cycle in a delayed model for tumor growth with quiescence, Nonlinear Analysis: Modelling and Control, 11 (2006), 95-110. Google Scholar

show all references

References:
[1]

J. A. Adam and B. Bellomo (ed), A Survey of Models on Tumour Immune Systems Dynamics, Birkhüauser, 1997.Google Scholar

[2]

E. O. AlzahraniA. AsiriM. M. El-Dessoky and Y. Kuang, Quiescence as an explanation of Gompertzian tumor growth revisited, Mathematical Biosciences, 254 (2014), 76-82. doi: 10.1016/j.mbs.2014.06.009. Google Scholar

[3]

E. O. Alzahrani and Y. Kuang, Nutrient limitations as an explanation of Gompertzian tumor growth, Discrete Cont. Dyn. Syst.-B., 21 (2016), 357-372. doi: 10.3934/dcdsb.2016.21.357. Google Scholar

[4]

R. P. Araujo and D. L. S. McElwain, A history of the study of solid tumour growth: The contribution of mathematical modelling, Bull. Math. Biol., 66 (2004), 1039-1091. doi: 10.1016/j.bulm.2003.11.002. Google Scholar

[5]

L. von Bertalanffy, Quantitative laws in metabolism and growth, Quart. Rev. Biol., 32 (1957), 217-231. doi: 10.1086/401873. Google Scholar

[6]

H. M. ByrneJ. R. KingD. L. S. McElwain and L. Preziosi, A two-phase model of solid tumour growth, Applied Mathematics Letters, 16 (2003), 567-573. doi: 10.1016/S0893-9659(03)00038-7. Google Scholar

[7]

S. E. Eikenberry, J. D. Nagy and Y. Kuang, The evolutionary impact of androgen levels on prostate cancer in a multi-scale mathematical model Biology Direct, 5 (2010), p24. doi: 10.1186/1745-6150-5-24. Google Scholar

[8]

S. E. Eikenberry, C. Thalhauser and Y. Kuang, Tumor-immune interaction, surgical treatment and cancer recurrence in a mathematical model of melanoma PLoS Comput. Biol. , 5 (2009), e1000362, 18pp. doi: 10.1371/journal.pcbi.1000362. Google Scholar

[9]

R. A. EverettY. ZhaoK. B. Flores and Y. Kuang, Data and implication based comparison of two chronic myeloid leukemia models, Math. Biosc. Eng., 10 (2013), 1501-1518. doi: 10.3934/mbe.2013.10.1501. Google Scholar

[10]

C. L. Frenzen and J. D. Murray, A cell kinetics justification for Gompertz equation, SIAM J. Appl. Math., 46 (1986), 614-629. doi: 10.1137/0146042. Google Scholar

[11]

S. Geritz and M. Gyllenberg, A mechanistic derivation of the DeAngelis-Beddington functional response, Journal of Theoretical Biology, 314 (2012), 106-108. doi: 10.1016/j.jtbi.2012.08.030. Google Scholar

[12]

B. Gompertz, On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies, Phil. Trans. Royal Soc. London, 115 (1825), 513-583. Google Scholar

[13]

M. Gyllenberg and G. F. Webb, Quiescence as an explanation of Gompertzian tumor growth, Growth Dev Aging, 53 (1989), 25-33. Google Scholar

[14]

M. Gyllenberg and G. F. Webb, A nonlinear structured population model of tumor growth with quiescence, J. Math. Biol., 28 (1990), 671-694. doi: 10.1007/BF00160231. Google Scholar

[15]

F. Kozusko and Z. Bajzer, Combining Gompertzian growth and cell population dynamics, Math. Biosc., 185 (2003), 153-167. doi: 10.1016/S0025-5564(03)00094-4. Google Scholar

[16]

Y. Kuang, Delay Differential Equations: With Applications in Population Dynamics, Academic Press, 1993. Google Scholar

[17]

Y. Kuang, J. D. Nagy and S. E. Eikenberry Introduction to Mathematical Oncology, CRC Press, 2016. Google Scholar

[18]

A. O. Martinez and R. J. Griego, Growth dynamics of multicell spheroids from three murine tumors, Growth, 44 (1980), 112-122. Google Scholar

[19]

M. Marusic and S. Vuk-Pavlovic, Prediction power of mathematical models for tumor growth, Journal of Biological Systems, 1 (1993), 69-78. Google Scholar

[20]

L. NortonR. SimonH. D. Brereton and A. E. Bogden, Predicting the course of Gompertzian growth, Nature, 264 (1976), 542-545. doi: 10.1038/264542a0. Google Scholar

[21]

T. Portz, Y. Kuang and J. D. Nagy, A clinical data validated mathematical model of prostate cancer growth under intermittent androgen suppression therapy AIP Advances, 2 (2012), 011002. doi: 10.1063/1.3697848. Google Scholar

[22]

J. A. Sherratt and M. J. Chaplain, A new mathematical model for avascular tumor growth, J. Math. Biol., 43 (2001), 291-312. doi: 10.1007/s002850100088. Google Scholar

[23]

C. J. ThalhauserT. SankarM. C. Preul and Y. Kuang, Explicit separation of growth and motility in a new tumor cord model, Bulletin of Math. Biol., 71 (2009), 585-601. doi: 10.1007/s11538-008-9372-8. Google Scholar

[24]

D. Wallace and X. Guo, Properties of tumor spheroid growth exhibited by simple mathematical models, Frontiers in Oncology, 3 (2013), 1-9. doi: 10.3389/fonc.2013.00051. Google Scholar

[25]

R. Yafia, Dynamics analysis and limit cycle in a delayed model for tumor growth with quiescence, Nonlinear Analysis: Modelling and Control, 11 (2006), 95-110. Google Scholar

Figure 1.  Bifurcation diagrams of model (2.4) with $f(r)= \frac{kr}{ar+1}$ and $g(r)= \frac{c}{r+m}$ using the cell proliferation time delay $\tau$ as the bifurcation parameter. The parameter values are $k=2, a=1, m=2, \mu=0.1, c=1, \theta=2/3.$ The positive steady state appears to be globally attractive for short time delay but lost its stability for larger values of $\tau$. As cell proliferation time delay increases, tumor size oscillates more noticeably and at a lower lever. Notice that the percentage of the average amount of proliferating cells decreases as $\tau$ increases
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