Advanced Search
Article Contents
Article Contents

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

Abstract / Introduction Related Papers Cited by
  • 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.
    Mathematics Subject Classification: 92C50, 97M60, 37B15, 68Q80.


    \begin{equation} \\ \end{equation}
  • [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), 257-274.


    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), 84-96.


    A. R. A. Anderson and M. A. J. Chaplain, Continuous and discrete mathematical models of tumor-induced angiogenesis, Bulletin of Mathematical Biology, 60 (1998), 857-900.


    M. Bernaschi and F. Castiglione, Design and implementation of an immune system simulator, Computers in Biology and Medicine, 31 (2001), 303-331.


    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), 903-931.


    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), 30-46.


    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), 142-150.


    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), 133-146.


    R. Bristow and R. Hill, Molecular and cellular basis of radiotherapy, in "The Basic Science of Oncology" (editors, I. Tannock and R. Hill), 295-321. McGraw Hill, New York, (1998).


    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), 1-17.


    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), 639-658.


    R. Cairns and R. Hill, Acute hypoxia enhances spontaneous lymph node metastasis in an orthotopic murine model of human cervical carcinoma, Cancer Research, 64 March (2004), 2054-2061.


    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), 28-36.


    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), 386-394.


    M. Chaplain and A. Matzavinos, Mathematical modelling of spatio-temporal phenomena in tumour immunology, Lect. Notes Math., 1872 (2006), 131-183.


    V. Cristini, J. Lowengrub and Q. Nie, Nonlinear simulation of tumor growth, J Math Biol., 46 (2003), 191-224.


    L. de Pillis, W. Gu and A. Radunskaya, Mixed immunotherapy and chemotherapy of tumors: Modeling applications and biological interpretations, Journal of Theoretical Biology, 238 September (2005), 841-862.


    L. de Pillis, D. Mallet and A. Radunskaya, Spatial tumor-immune modeling, Journal of Computational and Mathematical Models in Medicine, 7 June-September (2006), 159-276.


    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), 79-100.


    L. de Pillis and A. Radunskaya, The dynamics of an optimally controlled tumor model: A case study, Math Comput Model. (Special Issues), 37 (2003), 1221-1244.


    L. de Pillis and A. Radunskaya, Immune response to tumor invasion, in "Computational Fluid and Solid Mechanics" (editor, K. Bathe), M.I.T., 2 (2003), 1661-1668.


    L. de Pillis, A. Radunskaya and C. Wiseman, A validated mathematical model of cell-mediated immune response to tumor growth, Cancer Research, 65 September (2005), 7950-7958.


    M. Dewhirst, Concepts of oxygen transport at the microcirculatory level, Semin Radiat Oncol., 8 (1998), 143-50.


    S. Dormann and A. Deutsch, Modeling of self-organized avascular tumor growth with a hybrid cellular automaton, In Silico Biology, 2 (2002), 0035.


    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), 546-554.


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


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


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


    R. Gatenby and J. Gillies, Why do cancers have high aerobic glycolysis?, Nature Reviews Cancer, 4 (2004), 891-899.


    I. Georgoudas, G. Sirakoulis and I. Andreadis, An intelligent cellular automaton model for crowd evacuation in fire spreading conditions, in "19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007)" 1 36-43. IEEE Computer Society, (2007).


    S. Gobron and N. Chiba, Visual simulation of crack pattern based on 3D surface cellular automaton, in "Seventh International Conference on Parallel and Distributed Systems Workshops (ICPADS'00 Workshops)", pages 181-187. IEEE Computer Society, (2000).


    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), 1113-1118.


    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), 309-315.


    J. Haier and G. Nicolson, Role of tumor cell adhesion as an important factor in formation of distant metastases, Diseases Colon Rect., 44 (2001), 876-884.


    A. Harris, "Hypoxia - A Key Regulatory Factor in Tumour Growth," 2002.


    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), 1284-1291.


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


    M. Hystad and E. Rofstad, Oxygen consumption rate and mitochondrial density in human melanoma monolayer cultures and multicellular spheroids, Int J Cancer., 57 (1994), 532-537.


    T. L. Jackson, Vascular tumor growth and treatment: Consequenes of polyclonality, competition and dynamic vascular support, J Math Biol., 44 Mar.(2002), 201-226.


    S. Kooijman, "Dynamic Energy and Mass Budgets in Biological Systems," Cambridge University Press, Great Britain, 2nd edition, 2000.


    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), 1056-1060.


    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), 45651-45660.


    M. Li, Z. Ru and J. He, Cellular automata to simulate rock failure, in "16th International Conference on Artificial Reality and Telexistence-Workshops (ICAT'06)", 110-114. IEEE Computer Society, (2006).


    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), 765-798.doi: 10.1007/s00285-008-0216-9.


    D. Mallet and L. de Pillis, A cellular automata model of tumor-immune system interactions, Journal of Theoretical Biology, 239 (2006), 334-350.


    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), 7232-7240.


    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 December (1992), 11832-11836.


    D. Nelson and M. Cox, "Lehninger Principles of Biochemistry," W. H. Freeman and Co., 4th edition, 2004.


    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, Epub ahead of print, October 2006.


    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), 377-399.


    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), 315-331.


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


    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), 961-972.


    A. Radunskaya and M. Villasana, A delay differential equation model for tumor growth, J. Math.Biol., 47 (2003), 270-294.


    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 JUL 7 (2007), 186-204.


    K. A. Rejniak and A. R. A. Anderson, Hybrid models of tumor growth, Wiley Interdisciplinary Reviews - Systems Biology and Medicine, 3 Jan-Feb (2011), 115-125.


    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 April (2010), 411-423.


    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), 1377-93.


    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 (Supplement 1) (2007), S120 - S134. Proceedings of the International Brain Mapping & Intraoperative Surgical Planning Society Annual Meeting, (2006).


    P. Schornack and R. Gillies, Contributions of cell metabolism and $H^+$ diffusion to the acidic pH of tumors, Neoplasia, 5 (2003), 135-145.


    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), 977-981.


    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), 421-428.


    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), 703-713.


    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), 476-484.


    J. Smolle, Cellular automaton simulation of tumour growth - equivocal relationships between simulation parameters and morphologic pattern features, Anal Cell Pathol., 17 (1998), 71-82.


    J. Smolle, R. Hofmann-Wellenhof and H. Kerl, Pattern interpretation by cellular automata (pica)-evaluation of tumour cell adhesion in human melanomas, Anal Cell Pathol., 7 (1994), 91-106.


    P. Subarsky and R. Hill, The hypoxic tumour microenvironment and metastatic progression, Clinical & Experimental Metastasis, 20 (2003), 237-250.


    I. Tufto, H. Lyng and E. K. Forstad, Vascular density in human melanoma xenografts: Relationship to angiogenesis, perfusion and necrosis, Cancer Letters, 123 (1998), 159-165.


    S. Turner, Using cell potential energy to model the dynamics of adhesive biological cells, Physical Review E, 71 (2005), pp.12. 041903.


    S. Turner, J. Sherratt, K. Painter and N. Savill, From a discrete to a continuous model of biological cell movement, Physical Review E, 69 (2004), 021910.


    I. van Leeuwen, C. Zonneveld and S. Kooijman, The embedded tumour: Host physiology is important for the evaluation of tumour growth, British Journal of Cancer, 89 (2003), 2254-2263.


    P. Vaupel, O. Thews, D. Kelleher and M. Hoeckel, Current status of knowledge and critical issues in tumor oxygenation. results from 25 years research in tumor pathophysiology, Adv Exp Med Biol., 454 (1998), 591-602.


    R. Venkatasubramanian, M. A. Henson and N. S. Forbes, Incorporating energy metabolism into a growth model of multicellular tumor spheroids, Journal of Theoretical Biology, 242 (2006), 440-453.


    J. von Neumann, "Theory of Self-Reproducing Automata," University of Illinois Press, 1966.


    S. Wise, J. Lowengrubb and V. Cristini, An adaptive multigrid algorithm for simulating solid tumor growth using mixture models, Mathematical and Computer Modelling, 53 January (2011), 1-20.doi: 10.1016/j.mcm.2010.07.007.


    X. Zu and M. Guppy, Cancer metabolism: Facts, fantasy, and fiction, Biochemical and Biophysical Research Communications, 313 (2004), 459-465.doi: 10.1016/j.bbrc.2003.11.136.

  • 加载中

Article Metrics

HTML views() PDF downloads(186) Cited by(0)

Access History



    DownLoad:  Full-Size Img  PowerPoint