From the mathematical kinetic, and stochastic game theory to modelling mutations, onset, progression and immune competition of cancer cells ✩

This paper deals with a review and critical analysis on the mathematical kinetic theory of active particles applied to the modelling of the very early stage of cancer phenomena, specifically mutations, onset, progression of cancer cells, and their competition with the immune system. The mathematical theory describes the dynamics of large systems of interacting entities whose microscopic state includes not only geometrical and mechanical variables, but also specific biological functions. Applications are focused on the modelling of complex biological systems where two scales at the level of genes and cells interact generating the heterogeneous onset of cancer phenomena. The analysis also refers to the derivation of tissue level models from the underlying description at the lower scales. The review is constantly linked to a critical analysis focused on various open problems including the ambitious objective of developing a mathematical theory for complex biological systems.

[1]  A. Bellouquid,et al.  Mathematical methods and tools of kinetic theory towards modelling complex biological systems , 2005 .

[2]  Andrea Tosin,et al.  Mathematical modeling of vehicular traffic: a discrete kinetic theory approach , 2007 .

[3]  P. Maini,et al.  Mathematical oncology: Cancer summed up , 2003, Nature.

[4]  Nicola Bellomo,et al.  Dynamics of tumor interaction with the host immune system , 1994 .

[5]  T. Hunter,et al.  Oncogenic kinase signalling , 2001, Nature.

[6]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[7]  M. Nowak,et al.  Evolutionary Dynamics of Biological Games , 2004, Science.

[8]  Nicola Bellomo,et al.  On the complexity of multiple interactions with additional reasonings about Kate, Jules and Jim , 2008, Math. Comput. Model..

[9]  C. Maley,et al.  Cancer is a disease of clonal evolution within the body1–3. This has profound clinical implications for neoplastic progression, cancer prevention and cancer therapy. Although the idea of cancer as an evolutionary problem , 2006 .

[10]  Maria Letizia Bertotti,et al.  Conservation laws and asymptotic behavior of a model of social dynamics , 2008 .

[11]  P. Maini,et al.  Mathematical modeling of cell population dynamics in the colonic crypt and in colorectal cancer , 2007, Proceedings of the National Academy of Sciences.

[12]  Rustom Antia,et al.  The role of models in understanding CD8+ T-cell memory , 2005, Nature Reviews Immunology.

[13]  Katsuhiro Nishinari,et al.  Physics of Transport and Traffic Phenomena in Biology: from molecular motors and cells to organisms , 2005 .

[14]  H. Othmer,et al.  Models of dispersal in biological systems , 1988, Journal of mathematical biology.

[15]  M. Kolev Mathematical modelling of the competition between tumors and immune system considering the role of the antibodies , 2003 .

[16]  B. Perthame,et al.  Kinetic Models for Chemotaxis and their Drift-Diffusion Limits , 2004 .

[17]  D. G. Mallet,et al.  Spatial tumor-immune modeling , 2006 .

[18]  C. Schmeiser,et al.  MODEL HIERARCHIES FOR CELL AGGREGATION BY CHEMOTAXIS , 2006 .

[19]  A. d’Onofrio TUMOR-IMMUNE SYSTEM INTERACTION: MODELING THE TUMOR-STIMULATED PROLIFERATION OF EFFECTORS AND IMMUNOTHERAPY , 2006 .

[20]  Luigi Preziosi,et al.  On a kinetic (cellular) theory for competition between tumors and the host immune system , 1996 .

[21]  Johan Paulsson,et al.  Models of stochastic gene expression , 2005 .

[22]  W. Bodmer,et al.  Failure of programmed cell death and differentiation as causes of tumors: some simple mathematical models. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[23]  M. Lachowicz MICRO AND MESO SCALES OF DESCRIPTION CORRESPONDING TO A MODEL OF TISSUE INVASION BY SOLID TUMOURS , 2005 .

[24]  C. Cattani,et al.  On a mathematical model of immune competition , 2006, Appl. Math. Lett..

[25]  T. Vincent,et al.  An evolutionary model of carcinogenesis. , 2003, Cancer research.

[26]  E. Angelis,et al.  Qualitative analysis of a mean field model of tumor-immune system competition , 2003 .

[27]  L D Greller,et al.  Tumor heterogeneity and progression: conceptual foundations for modeling. , 1996, Invasion & metastasis.

[28]  C. Cattani,et al.  HYBRID TWO SCALES MATHEMATICAL TOOLS FOR ACTIVE PARTICLES MODELLING COMPLEX SYSTEMS WITH LEARNING HIDING DYNAMICS , 2007 .

[29]  Marco Ajmone Marsan,et al.  Towards a mathematical theory of complex socio-economical systems by functional subsystems representation , 2008 .

[30]  B. Perthame,et al.  Derivation of hyperbolic models for chemosensitive movement , 2005, Journal of mathematical biology.

[31]  N. Bellomo,et al.  Complex multicellular systems and immune competition: new paradigms looking for a mathematical theory. , 2008, Current topics in developmental biology.

[32]  Tutut Herawan,et al.  Computational and mathematical methods in medicine. , 2006, Computational and mathematical methods in medicine.

[33]  J. Sherratt,et al.  Intercellular adhesion and cancer invasion: a discrete simulation using the extended Potts model. , 2002, Journal of theoretical biology.

[34]  Nicola Bellomo,et al.  Generalized kinetic (Boltzmann) models: mathematical structures and applications , 2002 .

[35]  Nicola Bellomo,et al.  Strategies of applied mathematics towards an immuno-mathematical theory on tumors and immune system interactions , 1998 .

[36]  Pierre Degond,et al.  Continuum limit of self-driven particles with orientation interaction , 2007, 0710.0293.

[37]  Nicola Bellomo,et al.  BIFURCATION ANALYSIS FOR A NONLINEAR SYSTEM OF INTEGRO-DIFFERENTIAL EQUATIONS MODELLING TUMOR-IMMUNE CELLS COMPETITION , 1999 .

[38]  T. Blankenstein The role of tumor stroma in the interaction between tumor and immune system. , 2005, Current opinion in immunology.

[39]  Horst R. Thieme,et al.  Mathematics in Population Biology , 2003 .

[40]  C. Woese A New Biology for a New Century , 2004, Microbiology and Molecular Biology Reviews.

[41]  G. Weisbuch,et al.  Immunology for physicists , 1997 .

[42]  A. Bellouquid,et al.  Mathematical Modeling of Complex Biological Systems: A Kinetic Theory Approach , 2006 .

[43]  Anirvan M. Sengupta,et al.  Mutation-selection networks of cancer initiation: tumor suppressor genes and chromosomal instability. , 2003, Journal of theoretical biology.

[44]  I. Tomlinson,et al.  A nonlinear mathematical model of cell turnover, differentiation and tumorigenesis in the intestinal crypt. , 2007, Journal of theoretical biology.

[45]  Leah Edelstein-Keshet,et al.  Mathematical models in biology , 2005, Classics in applied mathematics.

[46]  L. Bonilla,et al.  High-field limit of the Vlasov-Poisson-Fokker-Planck system: A comparison of different perturbation methods , 2000, cond-mat/0007164.

[47]  A. Friedman MATHEMATICAL ANALYSIS AND CHALLENGES ARISING FROM MODELS OF TUMOR GROWTH , 2007 .

[48]  Nicola Bellomo,et al.  Challenging mathematical problems in cancer modelling , 2007 .

[49]  A. Aderem,et al.  A systems approach to dissecting immunity and inflammation. , 2004, Seminars in immunology.

[50]  M. L. Martins,et al.  Multiscale models for the growth of avascular tumors , 2007 .

[51]  C. A. Condat,et al.  Modeling Cancer Growth , 2001 .

[52]  A. Fasano,et al.  A parabolic-hyperbolic free boundary problem , 1986 .

[53]  J A Sherratt,et al.  Pattern formation and spatiotemporal irregularity in a model for macrophage-tumour interactions. , 1997, Journal of theoretical biology.

[54]  Michael Herty,et al.  Optimal Treatment Planning in Radiotherapy Based on Boltzmann Transport Equations , 2008 .

[55]  N. Komarova STOCHASTIC MODELING OF LOSS- AND GAIN-OF-FUNCTION MUTATIONS IN CANCER , 2007 .

[56]  K. Kinzler,et al.  Cancer genes and the pathways they control , 2004, Nature Medicine.

[57]  G. Webb Theory of Nonlinear Age-Dependent Population Dynamics , 1985 .

[58]  B. Perthame Transport Equations in Biology , 2006 .

[59]  H. Othmer,et al.  The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. , 2003, Journal of theoretical biology.

[60]  Dominik Wodarz,et al.  The optimal rate of chromosome loss for the inactivation of tumor suppressor genes in cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[61]  Sergio Albeverio,et al.  STOCHASTIC DYNAMICS OF VISCOELASTIC SKEINS: CONDENSATION WAVES AND CONTINUUM LIMITS , 2008 .

[62]  Nicola Bellomo,et al.  Methods and tools of the mathematical Kinetic theory toward modeling complex biological systems , 2006 .

[63]  Hans G. Othmer,et al.  The Diffusion Limit of Transport Equations II: Chemotaxis Equations , 2002, SIAM J. Appl. Math..

[64]  E. Birney,et al.  Cancer and genomics , 2001, Nature.

[65]  Nicola Bellomo,et al.  On the coupling of higher and lower scales using the mathematical kinetic theory of active particles , 2009, Appl. Math. Lett..

[66]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[67]  Bertrand Lods,et al.  On the kinetic theory for active particles: A model for tumor-immune system competition , 2008, Math. Comput. Model..

[68]  N. Komarova Spatial Stochastic Models for Cancer Initiation and Progression , 2006, Bulletin of mathematical biology.

[69]  Nicola Bellomo,et al.  From microscopic to macroscopic description of multicellular systems and biological growing tissues , 2007, Comput. Math. Appl..

[70]  C. Schmeiser,et al.  Kinetic models for chemotaxis: Hydrodynamic limits and spatio-temporal mechanisms , 2005, Journal of mathematical biology.

[71]  Filippo Castiglione,et al.  Modeling and simulation of cancer immunoprevention vaccine , 2005, Bioinform..

[72]  A. Bellouquid,et al.  Kinetic (cellular) models of cell progression and competition with the immune system , 2004 .

[73]  Francesco Pappalardo,et al.  MODELING TUMOR IMMUNOLOGY , 2006 .

[74]  P. Calvert,et al.  Mathematical tools , 1975, Nature.

[75]  N. Bellomo,et al.  From a class of kinetic models to the macroscopic equations for multicellular systems in biology , 2003 .

[76]  Emmanuel Tannenbaum,et al.  Semiconservative replication, genetic repair, and many-gened genomes: Extending the quasispecies paradigm to living systems , 2005 .

[77]  Alissa M. Weaver,et al.  Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment , 2006, Cell.

[78]  Andrzej Świerniak,et al.  Different Models of Chemotherapy Taking Into Account Drug Resistance Stemming from Gene Amplification , 2003 .

[79]  D. Waxman A model of population genetics and its mathematical relation to quantum theory , 2002 .

[80]  Lobna Derbel,et al.  ANALYSIS OF A NEW MODEL FOR TUMOR-IMMUNE SYSTEM COMPETITION INCLUDING LONG-TIME SCALE EFFECTS , 2004 .

[81]  Nicola Bellomo,et al.  On the foundations of cancer modelling: Selected topics, speculations, and perspectives , 2008 .

[82]  Luca Mesin,et al.  Modeling of the immune response: conceptual frameworks and applications , 2001 .

[83]  E. Angelis,et al.  Mathematical models of therapeutical actions related to tumour and immune system competition , 2005 .

[84]  S. Baylin,et al.  Epigenetic gene silencing in cancer – a mechanism for early oncogenic pathway addiction? , 2006, Nature Reviews Cancer.

[85]  Mikhail K. Kolev,et al.  A mathematical model for single cell cancer - Immune system dynamics , 2005, Math. Comput. Model..

[86]  N. Bellomo,et al.  MULTICELLULAR BIOLOGICAL GROWING SYSTEMS: HYPERBOLIC LIMITS TOWARDS MACROSCOPIC DESCRIPTION , 2007 .

[87]  Radek Erban,et al.  From Individual to Collective Behavior in Bacterial Chemotaxis , 2004, SIAM J. Appl. Math..

[88]  M. A. Herrero On the role of mathematics in biology , 2007, Journal of mathematical biology.

[89]  M. Nowak,et al.  Dynamics of cancer progression , 2004, Nature Reviews Cancer.

[90]  Marek Kimmel,et al.  Mathematical model of tumor invasion along linear or tubular structures , 2005, Math. Comput. Model..

[91]  Nicola Bellomo,et al.  Selected topics in cancer modeling : genesis, evolution, immune competition, and therapy , 2008 .

[92]  S. Rafii,et al.  VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche , 2005, Nature.

[93]  S. Frank Dynamics of Cancer: Incidence, Inheritance, and Evolution , 2007 .

[94]  Helen Moore,et al.  A mathematical model for chronic myelogenous leukemia (CML) and T cell interaction. , 2004, Journal of theoretical biology.

[95]  Addolorata Marasco,et al.  Bifurcation analysis for a mean field modelling of tumor and immune system competition , 2003 .

[96]  I. Brazzoli,et al.  On the Discrete Kinetic Theory for Active Particles. Modelling the Immune Competition , 2006 .

[97]  R. Rudnicki,et al.  A DISCRETE MODEL OF EVOLUTION OF SMALL PARALOG FAMILIES , 2007 .

[98]  Mikhail Kolev,et al.  Mathematical modeling of the competition between acquired immunity and cancer , 2003 .

[99]  A. Anderson,et al.  A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion , 2005 .

[100]  N. Bellomo,et al.  Looking for new paradigms towards a biological-mathematical theory of complex multicellular systems , 2006 .

[101]  Bruno Carbonaro,et al.  A second step towards a stochastic mathematical description of human feelings , 2005, Math. Comput. Model..

[102]  D. Kirschner,et al.  Modeling immunotherapy of the tumor – immune interaction , 1998, Journal of mathematical biology.

[103]  P. Nowell Tumor progression: a brief historical perspective. , 2002, Seminars in cancer biology.

[104]  M. Kimmel,et al.  MODELLING OF EARLY LUNG CANCER PROGRESSION: INFLUENCE OF GROWTH FACTOR PRODUCTION AND COOPERATION BETWEEN PARTIALLY TRANSFORMED CELLS , 2007 .

[105]  Michael Herty,et al.  Optimal treatment planning in radiotherapy based on Boltzmann transport calculations , 2007 .

[106]  R. Weinberg,et al.  The Biology of Cancer , 2006 .

[107]  Frank Mueller,et al.  Preface , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[108]  S. Spencer,et al.  An ordinary differential equation model for the multistep transformation to cancer. , 2004, Journal of theoretical biology.

[109]  O. Diekmann Mathematical Epidemiology of Infectious Diseases , 1996 .

[110]  Nicola Bellomo,et al.  Modeling Complex Living Systems: A Kinetic Theory and Stochastic Game Approach , 2007 .

[111]  Jack T. Trevors,et al.  Self-organization vs. self-ordering events in life-origin models , 2006 .

[112]  Luisa Arlotti,et al.  A Kinetic Model of Tumor/Immune System Cellular Interactions , 2002 .

[113]  Nicola Bellomo,et al.  A Survey of Models for Tumor-Immune System Dynamics , 1996 .

[114]  M. A. Herrero,et al.  FROM THE PHYSICAL LAWS OF TUMOR GROWTH TO MODELLING CANCER PROCESSES , 2006 .

[115]  M. Reed Why Is Mathematical Biology So Hard ? , 2004 .

[116]  N. Bellomo,et al.  On the onset of non-linearity for diffusion models of binary mixtures of biological materials by asymptotic analysis , 2006 .

[117]  Helen M. Byrne,et al.  MODELLING SCAFFOLD OCCUPATION BY A GROWING, NUTRIENT-RICH TISSUE , 2007 .

[118]  R. May Uses and Abuses of Mathematics in Biology , 2004, Science.

[119]  Z. Agur,et al.  LONG-RANGE PREDICTABILITY IN MODELS OF CELL POPULATIONS SUBJECTED TO PHASE-SPECIFIC DRUGS: GROWTH-RATE APPROXIMATION USING PROPERTIES OF POSITIVE COMPACT OPERATORS , 2006 .