From the mathematical kinetic theory of active particles to multiscale modelling of complex biological systems

The mathematical approach proposed in this paper refers to the modelling of large systems of interacting entities whose microscopic state includes not only mechanical variables (typically position and velocity), but also specific activities of the single entity. Their number is sufficiently large to describe the overall state of the system by a suitable probability distribution over the microscopic state. The first part of the paper is devoted to the derivation of mathematical structures which can be properly used to model a variety of models in different fields of applied sciences. Then, some research perspectives are focused on applications to biological systems.

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

[2]  Nicola Bellomo,et al.  Mathematical Topics in Nonlinear Kinetic Theory II - The Enskog Equation , 1991, Series on Advances in Mathematics for Applied Sciences.

[3]  W Arber,et al.  Genetic variation: molecular mechanisms and impact on microbial evolution. , 2000, FEMS microbiology reviews.

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

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

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

[7]  P. Michel EXISTENCE OF A SOLUTION TO THE CELL DIVISION EIGENPROBLEM , 2006 .

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

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

[10]  P. Maini,et al.  MODELLING THE RESPONSE OF VASCULAR TUMOURS TO CHEMOTHERAPY: A MULTISCALE APPROACH , 2006 .

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

[12]  Arnaud Chauviere,et al.  On the discrete kinetic theory for active particles. Mathematical tools , 2006, Math. Comput. Model..

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

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

[15]  Nicola Bellomo,et al.  MATHEMATICAL TOPICS ON THE MODELLING COMPLEX MULTICELLULAR SYSTEMS AND TUMOR IMMUNE CELLS COMPETITION , 2004 .

[16]  C. Schaller,et al.  MATHEMATICAL MODELLING OF GLIOBLASTOMA TUMOUR DEVELOPMENT: A REVIEW , 2005 .

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

[18]  Pierre Resibois,et al.  Classical kinetic theory of fluids , 1977 .

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

[20]  H. Steen,et al.  A stochastic model of cancer initiation including a bystander effect. , 2006, Journal of theoretical biology.

[21]  A. Friedman,et al.  ANALYSIS OF A MATHEMATICAL MODEL OF TUMOR LYMPHANGIOGENESIS , 2005 .

[22]  Karen M Page,et al.  Mathematical models of the fate of lymphoma B cells after antigen receptor ligation with specific antibodies. , 2006, Journal of theoretical biology.

[23]  P. Friedl,et al.  Tuning immune responses: diversity and adaptation of the immunological synapse , 2005, Nature Reviews Immunology.

[24]  Mikhail K. Kolev,et al.  A mathematical model of cellular immune response to leukemia , 2005, Math. Comput. Model..

[25]  T. Vincent,et al.  EVOLUTIONARY DYNAMICS IN CARCINOGENESIS , 2005 .

[26]  Alexander R. A. Anderson,et al.  Computational Methods and Results for Structured Multiscale Models of Tumor Invasion , 2005, Multiscale Model. Simul..

[27]  Lee A. Segel,et al.  On the distribution of dominance in populations of social organisms , 1992 .

[28]  N. Komarova,et al.  Stochastic modeling of drug resistance in cancer. , 2006, Journal of theoretical biology.

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

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

[31]  Nicola Bellomo,et al.  From the Jager and Segel model to kinetic population dynamics nonlinear evolution problems and applications , 1999 .

[32]  Silvana De Lillo,et al.  Mathematical tools of the kinetic theory of active particles with some reasoning on the modelling progression and heterogeneity , 2007, Math. Comput. Model..

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

[34]  Maria Letizia Bertotti,et al.  FROM DISCRETE KINETIC AND STOCHASTIC GAME THEORY TO MODELLING COMPLEX SYSTEMS IN APPLIED SCIENCES , 2004 .

[35]  Youshan Tao,et al.  A PARABOLIC–HYPERBOLIC FREE BOUNDARY PROBLEM MODELLING TUMOR TREATMENT WITH VIRUS , 2007 .

[36]  Hans G. Othmer,et al.  The Diffusion Limit of Transport Equations Derived from Velocity-Jump Processes , 2000, SIAM J. Appl. Math..

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

[38]  Marek Kimmel,et al.  Transcriptional stochasticity in gene expression. , 2006, Journal of theoretical biology.

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

[40]  Elena De Angelis,et al.  Modelling complex systems in applied sciences; methods and tools of the mathematical kinetic theory for active particles , 2006, Math. Comput. Model..

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

[42]  Martin A Nowak,et al.  Genetic instability and clonal expansion. , 2006, Journal of theoretical biology.

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

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

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

[46]  E. Shakhnovich,et al.  Genetic instability and the quasispecies model. , 2006, Journal of theoretical biology.

[47]  J. Folkman Role of angiogenesis in tumor growth and metastasis. , 2002, Seminars in oncology.

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

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

[50]  S. McDougall,et al.  Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: clinical implications and therapeutic targeting strategies. , 2006, Journal of theoretical biology.

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

[52]  F. Schweitzer Brownian Agents and Active Particles , 2003, Springer Series in Synergetics.

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

[54]  J. M. Pastor,et al.  Super-rough dynamics on tumor growth , 1998 .

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

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

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

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

[59]  M. Chaplain,et al.  Mathematical modelling of cancer cell invasion of tissue , 2005, Math. Comput. Model..

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

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

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

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

[64]  Luisa Arlotti,et al.  A discrete boltzmann-type model of swarming , 2005, Math. Comput. Model..

[65]  R. Schreiber,et al.  Cancer immunoediting: from immunosurveillance to tumor escape , 2002, Nature Immunology.

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