2013, 10(1): 121-150. doi: 10.3934/mbe.2013.10.121

A multiple time-scale computational model of a tumor and its micro environment

1. 

University of California, Irvine, Dept. of Statistics, School of Information and Computer Science, 3019 Bren Hall, Irvine, CA 92617-5100, United States

2. 

Princeton University, Dept. of Computer Science, 35 Olden Street, Princeton, NJ 08540-5233, United States

3. 

Wesleyan University, Dept. of Mathematics and Computer Science, 265 Church St. Middletown, CT 06459, United States

4. 

Pomona College, Dept. of Mathematics, 610 N. College Ave., Claremont, CA 91711, United States

Received  September 2012 Revised  September 2012 Published  December 2012

Experimental evidence suggests that a tumor's environment may be critical to designing successful therapeutic protocols: Modeling interactions between a tumor and its environment could improve our understanding of tumor growth and inform approaches to treatment. This paper describes an efficient, flexible, hybrid cellular automaton-based implementation of numerical solutions to multiple time-scale reaction-diffusion equations, applied to a model of tumor proliferation. The growth and maintenance of cells in our simulation depend on the rate of cellular energy (ATP) metabolized from nearby nutrients such as glucose and oxygen. Nutrient consumption rates are functions of local pH as well as local concentrations of oxygen and other fuels. The diffusion of these nutrients is modeled using a novel variation of random-walk techniques. Furthermore, we detail the effects of three boundary update rules on simulations, describing their effects on computational efficiency and biological realism. Qualitative and quantitative results from simulations provide insight on how tumor growth is affected by various environmental changes such as micro-vessel density or lower pH, both of high interest in current cancer research.
Citation: Christopher DuBois, Jesse Farnham, Eric Aaron, Ami Radunskaya. A multiple time-scale computational model of a tumor and its micro environment. Mathematical Biosciences & Engineering, 2013, 10 (1) : 121-150. doi: 10.3934/mbe.2013.10.121
References:
[1]

T. Alarcón, H. M. Byrne and P. K. Maini, A cellular automaton model for tumour growth in inhomogeneous environment,, Journal of Theoretical Biology, 225 (2003), 257. Google Scholar

[2]

A. Alt-Holland, W. Zhang, A. Margulis and J. Garlick, Microenvironmental control of premalignant disease: the role of intercellular adhesion in the progression of squamous cell carcinoma,, Seminars in Cancer Biology, 15 (2005), 84. Google Scholar

[3]

A. R. A. Anderson and M. A. J. Chaplain, Continuous and discrete mathematical models of tumor-induced angiogenesis,, Bulletin of Mathematical Biology, 60 (1998), 857. Google Scholar

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M. Bernaschi and F. Castiglione, Design and implementation of an immune system simulator,, Computers in Biology and Medicine, 31 (2001), 303. Google Scholar

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A. Bertuzzi, A. d'Onofrio, A. Fasano and A. Gandolfi, Regression and regrowth of tumor cords following single dose anticancer treatment,, Bulletin of Mathematical Biology, 65 (2003), 903. Google Scholar

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A. Bertuzzi, A. Fasano, A. Gandolfi and C. Sinisgalli, ATP production and necrosis formation in a tumour spheroid model,, Mathematical Modelling of Natural Phenomena, 2 (2007), 30. Google Scholar

[7]

A. Bertuzzi, A. Fasano, A. Gandolfi and C. Sinisgalli, Necrotic core in EMT6/Ro tumour spheroids: Is it caused by an ATP deficit?,, Journal of Theoretical Biology, 262 (2010), 142. Google Scholar

[8]

B. Blouw, H. Song, T. Tihan, J. Bosze, N. Ferrara, H. Gerber, R. Johnson and G. Bergers, The hypoxic response of tumors is dependent on their microenvironment,, Cancer Cell, 4 (2003), 133. Google Scholar

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R. Bristow and R. Hill, Molecular and cellular basis of radiotherapy,, in, (1998), 295. Google Scholar

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H. Byrne and M. Chaplain, Modelling the role of cell-cell adhesion in the growth and development of carcinomas,, Mathematical and Computational Modelling, 24 (1996), 1. Google Scholar

[11]

H. Byrne and M. Chaplain, Free boundary value problems associated with the growth and development of multicellular spheroids,, European Journal of Applied Mathematics, 8 (1997), 639. Google Scholar

[12]

R. Cairns and R. Hill, Acute hypoxia enhances spontaneous lymph node metastasis in an orthotopic murine model of human cervical carcinoma,, Cancer Research, 64 (2004), 2054. Google Scholar

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J. Casciari and J. Rasey, Determination of the radiobiologically hypoxic fraction in multicellular spheroids from data on the uptake of [3H]fluoromisonidazole,, Radiat Res., 141 (1995), 28. Google Scholar

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J. Casciari, S. Sotirchos and R. Sutherland, Variations in tumor cell growth rates and metabolism with oxygen concentration, glucose concentration, and extracellular pH,, Journal of Cellular Physiology, 151 (1992), 386. Google Scholar

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M. Chaplain and A. Matzavinos, Mathematical modelling of spatio-temporal phenomena in tumour immunology,, Lect. Notes Math., 1872 (2006), 131. Google Scholar

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V. Cristini, J. Lowengrub and Q. Nie, Nonlinear simulation of tumor growth,, J Math Biol., 46 (2003), 191. Google Scholar

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L. de Pillis, W. Gu and A. Radunskaya, Mixed immunotherapy and chemotherapy of tumors: Modeling applications and biological interpretations,, Journal of Theoretical Biology, 238 (2005), 841. Google Scholar

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L. de Pillis, D. Mallet and A. Radunskaya, Spatial tumor-immune modeling,, Journal of Computational and Mathematical Models in Medicine, 7 (2006), 159. Google Scholar

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L. de Pillis and A. Radunskaya, A mathematical tumor model with immune resistance and drug therapy: An optimal control approach,, J Theor Med., 3 (2001), 79. Google Scholar

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L. de Pillis and A. Radunskaya, The dynamics of an optimally controlled tumor model: A case study,, Math Comput Model. (Special Issues), 37 (2003), 1221. Google Scholar

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L. de Pillis and A. Radunskaya, Immune response to tumor invasion,, in, 2 (2003), 1661. Google Scholar

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L. de Pillis, A. Radunskaya and C. Wiseman, A validated mathematical model of cell-mediated immune response to tumor growth,, Cancer Research, 65 (2005), 7950. Google Scholar

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P. Gassmann, J. Haier and G. Nicolson, Cell adhesion and invasion during secondary tumor formation,, Cancer Growth and Progression, 3 (2004). Google Scholar

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R. Gatenby and J. Gillies, Why do cancers have high aerobic glycolysis?,, Nature Reviews Cancer, 4 (2004), 891. Google Scholar

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I. Georgoudas, G. Sirakoulis and I. Andreadis, An intelligent cellular automaton model for crowd evacuation in fire spreading conditions,, in, 1 (2007), 36. Google Scholar

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S. Gobron and N. Chiba, Visual simulation of crack pattern based on 3D surface cellular automaton,, in, (2000), 181. Google Scholar

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[35]

A. Harris, "Hypoxia - A Key Regulatory Factor in Tumour Growth,", 2002., (). Google Scholar

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G. Helmlinger, A. Sckell, M. Dellian, N. Forbes and R. Jain, Acid production in glycolysis-impaired tumors provides new insights into tumor metabolism,, Clinical Cancer Research, 8 (2002), 1284. Google Scholar

[37]

M. Hockel and P. Vaupel, "Tumor Hypoxia: Definitions and Current Clinical, Biologic, and Molecular Aspects,", 2001., (). Google Scholar

[38]

M. Hystad and E. Rofstad, Oxygen consumption rate and mitochondrial density in human melanoma monolayer cultures and multicellular spheroids,, Int J Cancer., 57 (1994), 532. Google Scholar

[39]

T. L. Jackson, Vascular tumor growth and treatment: Consequenes of polyclonality, competition and dynamic vascular support,, J Math Biol., 44 (2002), 201. Google Scholar

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S. Kooijman, "Dynamic Energy and Mass Budgets in Biological Systems,", Cambridge University Press, (2000). Google Scholar

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M. I. Koukourakis, M. Pitiakoudis, A. Giatromanolaki, A. Tsarouha, A. Polychronidis, E. Sivridis and C. Simopoulos, Oxygen and glucose consumption in gastrointestinal adenocarcinomas: Correlation with markers of hypoxia, acidity and anaerobic glycolysis,, Cancer Science, 97 (2006), 1056. Google Scholar

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M. Kunz, S. Moeller, D. Koczan, P. Lorenz, R. Wenger, M. Glocker, H. Thiesen, G. Gross and S. Ibrahim, Mechanisms of hypoxic gene regulation of angiogenesis factor Cyr61 in melanoma cells,, Journal of Biological Chemistry, 278 (2003), 45651. Google Scholar

[43]

M. Li, Z. Ru and J. He, Cellular automata to simulate rock failure,, in, (2006), 110. Google Scholar

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P. Macklin, S. McDougall, A. Anderson, M. Chaplain, V. Cristini and J. Lowengrub, Multiscale modelling and nonlinear simulation of vascular tumour growth,, Journal of Mathematical Biology, 58 (2009), 765. doi: 10.1007/s00285-008-0216-9. Google Scholar

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show all references

References:
[1]

T. Alarcón, H. M. Byrne and P. K. Maini, A cellular automaton model for tumour growth in inhomogeneous environment,, Journal of Theoretical Biology, 225 (2003), 257. Google Scholar

[2]

A. Alt-Holland, W. Zhang, A. Margulis and J. Garlick, Microenvironmental control of premalignant disease: the role of intercellular adhesion in the progression of squamous cell carcinoma,, Seminars in Cancer Biology, 15 (2005), 84. Google Scholar

[3]

A. R. A. Anderson and M. A. J. Chaplain, Continuous and discrete mathematical models of tumor-induced angiogenesis,, Bulletin of Mathematical Biology, 60 (1998), 857. Google Scholar

[4]

M. Bernaschi and F. Castiglione, Design and implementation of an immune system simulator,, Computers in Biology and Medicine, 31 (2001), 303. Google Scholar

[5]

A. Bertuzzi, A. d'Onofrio, A. Fasano and A. Gandolfi, Regression and regrowth of tumor cords following single dose anticancer treatment,, Bulletin of Mathematical Biology, 65 (2003), 903. Google Scholar

[6]

A. Bertuzzi, A. Fasano, A. Gandolfi and C. Sinisgalli, ATP production and necrosis formation in a tumour spheroid model,, Mathematical Modelling of Natural Phenomena, 2 (2007), 30. Google Scholar

[7]

A. Bertuzzi, A. Fasano, A. Gandolfi and C. Sinisgalli, Necrotic core in EMT6/Ro tumour spheroids: Is it caused by an ATP deficit?,, Journal of Theoretical Biology, 262 (2010), 142. Google Scholar

[8]

B. Blouw, H. Song, T. Tihan, J. Bosze, N. Ferrara, H. Gerber, R. Johnson and G. Bergers, The hypoxic response of tumors is dependent on their microenvironment,, Cancer Cell, 4 (2003), 133. Google Scholar

[9]

R. Bristow and R. Hill, Molecular and cellular basis of radiotherapy,, in, (1998), 295. Google Scholar

[10]

H. Byrne and M. Chaplain, Modelling the role of cell-cell adhesion in the growth and development of carcinomas,, Mathematical and Computational Modelling, 24 (1996), 1. Google Scholar

[11]

H. Byrne and M. Chaplain, Free boundary value problems associated with the growth and development of multicellular spheroids,, European Journal of Applied Mathematics, 8 (1997), 639. Google Scholar

[12]

R. Cairns and R. Hill, Acute hypoxia enhances spontaneous lymph node metastasis in an orthotopic murine model of human cervical carcinoma,, Cancer Research, 64 (2004), 2054. Google Scholar

[13]

J. Casciari and J. Rasey, Determination of the radiobiologically hypoxic fraction in multicellular spheroids from data on the uptake of [3H]fluoromisonidazole,, Radiat Res., 141 (1995), 28. Google Scholar

[14]

J. Casciari, S. Sotirchos and R. Sutherland, Variations in tumor cell growth rates and metabolism with oxygen concentration, glucose concentration, and extracellular pH,, Journal of Cellular Physiology, 151 (1992), 386. Google Scholar

[15]

M. Chaplain and A. Matzavinos, Mathematical modelling of spatio-temporal phenomena in tumour immunology,, Lect. Notes Math., 1872 (2006), 131. Google Scholar

[16]

V. Cristini, J. Lowengrub and Q. Nie, Nonlinear simulation of tumor growth,, J Math Biol., 46 (2003), 191. Google Scholar

[17]

L. de Pillis, W. Gu and A. Radunskaya, Mixed immunotherapy and chemotherapy of tumors: Modeling applications and biological interpretations,, Journal of Theoretical Biology, 238 (2005), 841. Google Scholar

[18]

L. de Pillis, D. Mallet and A. Radunskaya, Spatial tumor-immune modeling,, Journal of Computational and Mathematical Models in Medicine, 7 (2006), 159. Google Scholar

[19]

L. de Pillis and A. Radunskaya, A mathematical tumor model with immune resistance and drug therapy: An optimal control approach,, J Theor Med., 3 (2001), 79. Google Scholar

[20]

L. de Pillis and A. Radunskaya, The dynamics of an optimally controlled tumor model: A case study,, Math Comput Model. (Special Issues), 37 (2003), 1221. Google Scholar

[21]

L. de Pillis and A. Radunskaya, Immune response to tumor invasion,, in, 2 (2003), 1661. Google Scholar

[22]

L. de Pillis, A. Radunskaya and C. Wiseman, A validated mathematical model of cell-mediated immune response to tumor growth,, Cancer Research, 65 (2005), 7950. Google Scholar

[23]

M. Dewhirst, Concepts of oxygen transport at the microcirculatory level,, Semin Radiat Oncol., 8 (1998), 143. Google Scholar

[24]

S. Dormann and A. Deutsch, Modeling of self-organized avascular tumor growth with a hybrid cellular automaton,, In Silico Biology, 2 (2002). Google Scholar

[25]

A. dos Reis, J. Mombach, M. Walter and de Avila L. F., The interplay between cell adhesion and environment rigidity in the morphology of tumors,, Phyisca A-Statistical Mechanics and its Applications, 322 (2003), 546. Google Scholar

[26]

R. D'Souza, N. Margolus and M. Smith, Dimension-splitting for simplifying diffusion in lattice-gas models,, Journal of Statistical Physics, 107 (2002). Google Scholar

[27]

S. C. Ferreira, M. L. Martins and M. J. Vilela, Reaction-diffusion model for the growth of avascular tumor,, Phys Rev E, 65 (2002). Google Scholar

[28]

P. Gassmann, J. Haier and G. Nicolson, Cell adhesion and invasion during secondary tumor formation,, Cancer Growth and Progression, 3 (2004). Google Scholar

[29]

R. Gatenby and J. Gillies, Why do cancers have high aerobic glycolysis?,, Nature Reviews Cancer, 4 (2004), 891. Google Scholar

[30]

I. Georgoudas, G. Sirakoulis and I. Andreadis, An intelligent cellular automaton model for crowd evacuation in fire spreading conditions,, in, 1 (2007), 36. Google Scholar

[31]

S. Gobron and N. Chiba, Visual simulation of crack pattern based on 3D surface cellular automaton,, in, (2000), 181. Google Scholar

[32]

M. Gryczynskia, J. Kobos and W. Pietruszewska, Intratumoral microvessels density and morphometric study of angiogenesis as prognostic factor in laryngeal cancer,, International Congress Series, 1240 (2003), 1113. Google Scholar

[33]

M. Guppy, P. Leedman, X. Zu and V. Russel, Contribution by different fuels and metabolic pathways to the total ATP turnover of proliferating MCF-7 breast cancer cells,, Biochem. J., 364 (2002), 309. Google Scholar

[34]

J. Haier and G. Nicolson, Role of tumor cell adhesion as an important factor in formation of distant metastases,, Diseases Colon Rect., 44 (2001), 876. Google Scholar

[35]

A. Harris, "Hypoxia - A Key Regulatory Factor in Tumour Growth,", 2002., (). Google Scholar

[36]

G. Helmlinger, A. Sckell, M. Dellian, N. Forbes and R. Jain, Acid production in glycolysis-impaired tumors provides new insights into tumor metabolism,, Clinical Cancer Research, 8 (2002), 1284. Google Scholar

[37]

M. Hockel and P. Vaupel, "Tumor Hypoxia: Definitions and Current Clinical, Biologic, and Molecular Aspects,", 2001., (). Google Scholar

[38]

M. Hystad and E. Rofstad, Oxygen consumption rate and mitochondrial density in human melanoma monolayer cultures and multicellular spheroids,, Int J Cancer., 57 (1994), 532. Google Scholar

[39]

T. L. Jackson, Vascular tumor growth and treatment: Consequenes of polyclonality, competition and dynamic vascular support,, J Math Biol., 44 (2002), 201. Google Scholar

[40]

S. Kooijman, "Dynamic Energy and Mass Budgets in Biological Systems,", Cambridge University Press, (2000). Google Scholar

[41]

M. I. Koukourakis, M. Pitiakoudis, A. Giatromanolaki, A. Tsarouha, A. Polychronidis, E. Sivridis and C. Simopoulos, Oxygen and glucose consumption in gastrointestinal adenocarcinomas: Correlation with markers of hypoxia, acidity and anaerobic glycolysis,, Cancer Science, 97 (2006), 1056. Google Scholar

[42]

M. Kunz, S. Moeller, D. Koczan, P. Lorenz, R. Wenger, M. Glocker, H. Thiesen, G. Gross and S. Ibrahim, Mechanisms of hypoxic gene regulation of angiogenesis factor Cyr61 in melanoma cells,, Journal of Biological Chemistry, 278 (2003), 45651. Google Scholar

[43]

M. Li, Z. Ru and J. He, Cellular automata to simulate rock failure,, in, (2006), 110. Google Scholar

[44]

P. Macklin, S. McDougall, A. Anderson, M. Chaplain, V. Cristini and J. Lowengrub, Multiscale modelling and nonlinear simulation of vascular tumour growth,, Journal of Mathematical Biology, 58 (2009), 765. doi: 10.1007/s00285-008-0216-9. Google Scholar

[45]

D. Mallet and L. de Pillis, A cellular automata model of tumor-immune system interactions,, Journal of Theoretical Biology, 239 (2006), 334. Google Scholar

[46]

C. Menon, G. Polin, I. Prabakaran, A. Hsi, C. Cheung, J. Culver, J. Pingpank, C. Sehgal, A. Yodh, D. Buerk and D. Fraker, An integrated approach to measuring tumor oxygen status using human melanoma xenografts as a model,, Cancer Research, 63 (2003), 7232. Google Scholar

[47]

B. Mueller, R. Reisfeld, T. Edgington and W. Ruf, Expression of tissue factor by melanoma cells promotes efficient hematogenous metastasis,, Proc. Natl. Acad. Sci. USA, 89 (1992), 11832. Google Scholar

[48]

D. Nelson and M. Cox, "Lehninger Principles of Biochemistry,", W. H. Freeman and Co., (2004). Google Scholar

[49]

N. Oriuchi, T. Higuchi, T. Ishikita, M. Miyakubo, H. Hanaoka, Y. Iida and K. Endo, Present role and future prospects of positron emission tomography in clinical oncology,, Cancer Science, (2006). Google Scholar

[50]

M. Owen, H. Byrne and C. Lewis, Mathematical modelling of the use of macrophages as vehicles for drug delivery to hypoxic tumour sites,, Journal of Theoretical Biology, 226 (2004), 377. Google Scholar

[51]

A. Patel, E. Gawlinski, S. Lemieux and R. Gatenby, A cellular automaton model of early tumor growth and invasion: The effects of native tissue vascularity and increased anaerobic tumor metabolism,, Journal of Theoretical Biology, 213 (2001), 315. Google Scholar

[52]

L. Preziosi, "Cancer Modelling and Simulation,", Mathematical Biology and Medicine Series. Chapman & Hall/CRC, (2003). Google Scholar

[53]

R. Puzone, B. Kohler, P. Seiden and F. Celada, IMMSIM, a flexible model for in machina experiments on immune system responses,, Future Generation Computer Systems, 18 (2002), 961. Google Scholar

[54]

A. Radunskaya and M. Villasana, A delay differential equation model for tumor growth,, J. Math.Biol., 47 (2003), 270. Google Scholar

[55]

K. A. Rejniak, An immersed boundary framework for modelling the growth of individual cells: An application to the early tumour development,, Journal of Theoretical Biology, 247 (2007), 186. Google Scholar

[56]

K. A. Rejniak and A. R. A. Anderson, Hybrid models of tumor growth,, Wiley Interdisciplinary Reviews - Systems Biology and Medicine, 3 (2011), 115. Google Scholar

[57]

K. A. Rejniak and L. J. McCawley, Current trends in mathematical modeling of tumor-microenvironment interactions: a survey of tools and applications,, Experimental Biology and Medicine, 235 (2010), 411. Google Scholar

[58]

E. Rofstad and K. Maseide, Radiobiological and immunohistochemical assessment of hypoxia in human melanoma xenografts: acute and chronic hypoxia in individual tumours,, Int J Radiat Biol., 75 (1999), 1377. Google Scholar

[59]

S. Sanga, H. B. Frieboes, X. Zheng, R. Gatenby, E. L. Bearer and V. Cristini, Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth,, NeuroImage, 37 (2007). Google Scholar

[60]

P. Schornack and R. Gillies, Contributions of cell metabolism and $H^+$ diffusion to the acidic pH of tumors,, Neoplasia, 5 (2003), 135. Google Scholar

[61]

T. J. Schulz, R. Thierbach, A. Voigt, G. Drewes, B. Mietzner, P. Steinberg, A. F. H. Pfeiffer and M. Ristow, Induction of oxidative metabolism by mitochondrial frataxin inhibits cancer growth: Otto warburg revisited,, J. Biol. Chem., 281 (2006), 977. Google Scholar

[62]

R. Skoyum, K. Eide, K. Berg and E. Rofstad, Energy metabolism in human melanoma cells under hypoxic and acidic conditions in vitro,, Br J Cancer, 76 (1997), 421. Google Scholar

[63]

K. Smallbone, R. A. Gatenby, R. Gillies, P. K. Maini and D. Gavaghan, Metabolic changes during carcinogenesis: Potential impact on invasiveness,, Journal of Theoretical Biology, 244 (2006), 703. Google Scholar

[64]

K. Smallbone, D. J. Gavaghan, R. A. Gatenby and P. K. Maini, The role of acidity in solid tumour growth and invasion,, Journal of Theoretical Biology, 234 (2005), 476. Google Scholar

[65]

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