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A Bistable Field Model of Cancer Dynamics

Published online by Cambridge University Press:  20 August 2015

C. Cherubini*
Affiliation:
Nonlinear Physics and Mathematical Modeling Lab, University Campus Bio-Medico, I-00128, Via A. del Portillo 21, Rome, Italy International Center for Relativistic Astrophysics-I.C.R.A., University of Rome “La Sapienza”, I-00185 Rome, Italy
A. Gizzi*
Affiliation:
Nonlinear Physics and Mathematical Modeling Lab, University Campus Bio-Medico, I-00128, Via A. del Portillo 21, Rome, Italy Alberto Sordi Foundation-Research Institute on Aging, I-00128 Rome, Italy
M. Bertolaso*
Affiliation:
Institute of Philosophy of Scientific and Technological Activity, University Campus Bio-Medico, I-00128, Via A. del Portillo 21, Rome, Italy
V. Tambone*
Affiliation:
Institute of Philosophy of Scientific and Technological Activity, University Campus Bio-Medico, I-00128, Via A. del Portillo 21, Rome, Italy
S. Filippi*
Affiliation:
Nonlinear Physics and Mathematical Modeling Lab, University Campus Bio-Medico, I-00128, Via A. del Portillo 21, Rome, Italy International Center for Relativistic Astrophysics-I.C.R.A., University of Rome “La Sapienza”, I-00185 Rome, Italy
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Abstract

Cancer spread is a dynamical process occurring not only in time but also in space which, for solid tumors at least, can be modeled quantitatively by reaction and diffusion equations with a bistable behavior: tumor cell colonization happens in a portion of tissue and propagates, but in some cases the process is stopped. Such a cancer proliferation/extintion dynamics is obtained in many mathematical models as a limit of complicated interacting biological fields. In this article we present a very basic model of cancer proliferation adopting the bistable equation for a single tumor cell dynamics. The reaction-diffusion theory is numerically and analytically studied and then extended in order to take into account dispersal effects in cancer progression in analogy with ecological models based on the porous medium equation. Possible implications of this approach for explanation and prediction of tumor development on the lines of existing studies on brain cancer progression are discussed. The potential role of continuum models in connecting the two predominant interpretative theories about cancer, once formalized in appropriate mathematical terms, is discussed.

Type
Research Article
Copyright
Copyright © Global Science Press Limited 2012

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References

[1]D’Arcy, W.T., On Growth and Form, Cambridge University Press, 1992.Google Scholar
[2]Bini, D., Cherubini, C., Filippi, S., Gizzi, A. and Ricci, P. E., On spiral waves arising in natural systems, Commun. Comput. Phys., 8 (2010), 610–622.Google Scholar
[3]Murray, J. D., Mathematical Biology, 3rd edition in 2 volumes, Springer, 2004.Google Scholar
[4]Winfree, A. T., The Geometry of Biological Time, 2nd edition, Springer, 2001.Google Scholar
[5]Winfree, A. T., When Time Breaks Down: The Three-Dimensional Dynamics of Electrochemical Waves and Cardiac Arrhythmias, Princeton University Press, 1987.Google Scholar
[6]Jean, R. V., Phyllotaxis: A Systemic Study in Plant Morphogenesis, Cambridge University Press, 1994.Google Scholar
[7]Kuramoto, Y., Chemical Oscillations, Waves and Turbulence, Dover, 2003.Google Scholar
[8]Kondepudi, D. and Prigogine, I., Modern Thermodynamics: From Heat Engines to Dissipa-tive Structures, Wiley, 1998.Google Scholar
[9]Epstein, I. R. and Pojman, J. A., An Introduction to Nonlinear Chemical Dynamics, Oxford University Press, 1998.Google Scholar
[10]Turing, A. M., The chemical basis of morphogenesis, Phil. Trans. Roy. Soc. B, 237 (1952), 37–72.Google Scholar
[11]Panetta, C., Chaplain, M. A. J. and Adam, J., The mathematical modelling of cancer: a review, Proceedings of the MMMHS Conference (eds. Horn, M. A., Simonett, G. and Webb, G.), Vanderbilt University Press, 1998.Google Scholar
[12]Fearon, E. R. and Vogelstein, B., A genetic model for colorectal tumorigenesis, Cell, 61 (1990), 759–767.Google Scholar
[13]Soto, A. M. and Sonnenschein, C., The somatic mutation theory of cancer: growing problems with the paradigm, BioEssays, 26 (2004), 1097–1107.Google Scholar
[14]Sonnenschein, C. and Soto, A. M. AM, Theories of carcinogenesis: an emerging perspective, Semin. Cancer Biol., 18 (2008), 372–377.Google Scholar
[15]Potter, J. D., Morphostats, morphogens, microarchitecture and malignancy, Nat. Rev. Cancer, 7 (2007), 464–474.Google Scholar
[16]Weinberg, R. A., One Renegade Cell: How Cancer Begins, New York, Basic Books, 1998.Google Scholar
[17]Sonnenschein, C. and Soto, A. M., Somatic mutation theory of carcinogenesis: why it should be dropped and replaced, Mol. Carcinog., 29 (2000), 205–211.Google Scholar
[18]Baker, S. G., Soto, A. M., Sonnenschein, C., Cappuccio, A., Potter, J. D. and Kramer, B. S., Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats, BMC Cancer, 9 (2009), 89.Google Scholar
[19]Wang, X., Kam, Z., Carlton, P. M., Xu, L., Sedat, J. W. and Blackburn, E. H., Rapid telomere motions in live human cells analyzed by highly time-resolved microscopy, Epigenetics & Chromatin, 1 (2008), 4.Google Scholar
[20]Bertolaso, M., Towards and integrated view of the neoplastic phenomena in cancer research, Hist. Phil. Life Sci., 31 (2009), 79–98.Google Scholar
[21]Wolfram, S., A New Kind of Science, Wolfram Media, 1st edition, 2002.Google Scholar
[22]Chopard, B. and Droz, M., Cellular Automata Modeling of Physical Systems, Cambridge University Press, 1998.Google Scholar
[23]Lefever, R. and Horsthemke, W., Bistability in fluctuating envitonments, implications in tumor immunology, Bull. Math. Biol., 41 (1979), 269–290.Google Scholar
[24]Wodarz, D. and Komarova, N.L., Computational Biology of Cancer: Lecture Notes and Mathematical Modeling, World Scientific, 2005.Google Scholar
[25]Swanson, K. R., Alvord, E. C. and Murray, J. D., A quantitative model for differential motility of gliomas in grey and white matter, Cell Proliferation, 33(5) (2000), 317–330.Google Scholar
[26]Swanson, K. R., Alvord, E. C. Jr and Murray, J. D., Quantifying efficacy of chemotherapy of brain tumors (gliomas) with homogeneous and heterogeneous drug delivery, Acta Biotheo-retica, 50(4) (2002), 223–237.Google Scholar
[27]Swanson, K. R., Alvord, E. C. Jr. and Murray, J. D., Virtual resection of gliomas: effects of location and extent of resection on recurrence, Math. Comput. Model., 37 (2003), 1177–1190.Google Scholar
[28]Swanson, K. R., Bridge, C., Murray, J. D. and Alvord, E. C. Jr, Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion, J. Neurological Sci., 216(1) (2003), 1–10.Google Scholar
[29]Swanson, K. R., Alvord, E. C. Jr. and Murray, J. D., Dynamics of a model fro brain tumors reveals a small window for therapeutic intervention, Disc. Cont. Dyn. Syst. B, 4(1) (2004), 289–295.Google Scholar
[30]Harpold, H. L. P., Alvord, E. C. Jr. and Swanson, K. R., Visualizing beyond the tip of the iceberg: the evolution of mathematical modeling of glioma growth and invasion, J. Neu-ropathol. Exp. Neurol., 66 (2007), 1–9.Google Scholar
[31]Bini, D., Cherubini, C. and Filippi, S., Heat transfer in FitzHugh-Nagumo model, Phys. Rev. E, 74 (2006), 041905.Google Scholar
[32]Cherubini, C., Filippi, S., Nardinocchi, P. and Teresi, L., An electromechanical model of cardiac tissue: constitutive issues and electrophysiological effects, Prog. Biophys. Mol. Biol., 97 (2008), 562–573.CrossRefGoogle ScholarPubMed
[33]Bini, D., Cherubini, C. and Filippi, S., Viscoelastic FitzHugh-Nagumo models, Phys. Rev. E, 72 (2005), 041929.Google Scholar
[34]Bini, D., Cherubini, C. and Filippi, S., On vortices heating biological excitable media, Chaos, Solitons & Fractals, 42 (2009), 2057–2066.Google Scholar
[35]Keener, J. and Sneyd, J., Mathematical Physiology, Springer, 2001.Google Scholar
[36]Strogatz, S. H., Nonlinear Dynamics and Chaos: with Applications to Physics, Biology, Chemistry and Engineering, Westview Press, 2001.Google Scholar
[37]Cherubini, C., Gizzi, A., Bertolaso, M., Tambone, V. and Filippi, S., in preparation, 2010.Google Scholar
[38]Baker, S. G., Cappuccio, A. and Potter, J. D., Research on early-stage carcinogenesis: are we approaching paradigm instability?, J. Clin. Oncol., 28 (2010), 3215–3218.Google Scholar
[39]Cherubini, C., Filippi, S. and Gizzi, A., Diffusion processes in human brain using COMSOL multiphysics, Proceedings of COMSOL Users Conference of Milan, Italy, 2006.Google Scholar
[40]Marco, D. E., Cannas, S. A., Montemurro, M. A., Hu, B. and Cheng, S. Y., Comparable ecological dynamics underlie early cancer invasion and species dispersal, involving self-organizing processes, J. Theor. Biol., 256 (2009), 65–75.Google Scholar
[41]Morton, K. W. and Mayers, D. F., Numerical Solutions of Partial Differential Equations, Cambridge University Press, 1994.Google Scholar
[42]Chicoine, M. R. and Silbergeld, D. L., Assessment of brain tumor cell motility in vitro and in vivo, J. Neurosurg., 82 (1995), 615–622.CrossRefGoogle Scholar