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Journal ArticleDOI

Monte Carlo simulation of the shape space model of immunology

01 Nov 1992-Physica A-statistical Mechanics and Its Applications (North-Holland)-Vol. 189, Iss: 3, pp 403-410
TL;DR: In this paper, the shape space model of de Boer, Segel and Perelson for the immune system is studied with a probabilistic updating rule by Monte Carlo simulation, and a suitable mathematical form is chosen for the probability of increase of B-cell concentration depending on the concentration around the mirror image site.
Abstract: The shape space model of de Boer, Segel and Perelson for the immune system is studied with a probabilistic updating rule by Monte Carlo simulation. A suitable mathematical form is chosen for the probability of increase of B-cell concentration depending on the concentration around the mirror image site. The results obtained agree reasonably with the results obtained by deterministic cellular automata.
Citations
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Journal ArticleDOI
TL;DR: A body of work on computational immune systems that behave analogously to the natural immune system and in some cases have been used to solve practical engineering problems such as computer security are described.
Abstract: This review describes a body of work on computational immune systems that behave analogously to the natural immune system. These artificial immune systems (AIS) simulate the behavior of the natural immune system and in some cases have been used to solve practical engineering problems such as computer security. AIS have several strengths that can complement wet lab immunology. It is easier to conduct simulation experiments and to vary experimental conditions, for example, to rule out hypotheses; it is easier to isolate a single mechanism to test hypotheses about how it functions; agent-based models of the immune system can integrate data from several different experiments into a single in silico experimental system.

1,021 citations

Journal ArticleDOI
TL;DR: A brief introduction to the biology of the immune system is provided and a number of immunological problems in which the use of physical concepts and mathematical methods has increased the authors' understanding are discussed.
Abstract: The immune system is a complex system of cells and molecules that can provide us with a basic defense against pathogenic organisms. Like the nervous system, the immune system performs pattern recognition tasks, learns, and retains a memory of the antigens that it has fought. The immune system contains more than 10{sup 7} different clones of cells that communicate via cell-cell contact and the secretion of molecules. Performing complex tasks such as learning and memory involves cooperation among large numbers of components of the immune system and hence there is interest in using methods and concepts from statistical physics. Furthermore, the immune response develops in time and the description of its time evolution is an interesting problem in dynamical systems. In this paper, the authors provide a brief introduction to the biology of the immune system and discuss a number of immunological problems in which the use of physical concepts and mathematical methods has increased our understanding. {copyright} {ital 1997} {ital The American Physical Society}

591 citations

Journal ArticleDOI
TL;DR: Various agent-based models relevant to host–pathogen systems and their contributions to the authors' understanding of biological processes are reviewed and some limitations and challenges are pointed out.

205 citations


Cites background from "Monte Carlo simulation of the shape..."

  • ...Another variety of ABMs of host–pathogen/immune systems are called shape space models [27,28,61,83,84,92,95,98]....

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Journal ArticleDOI
TL;DR: Optimisation and parallelisation solutions are discussed, with reference to existing MC simulation code for dynamics of HIV infection, for large-scale simulations of the immune system response to infection.

19 citations


Cites methods from "Monte Carlo simulation of the shape..."

  • ...Since then MC method has been used to model a large variety of problems, from pi estimation to Schrödinger equation eigenvalues approximation and immune system simulation Dasgupta [11], Mannion and al [12]....

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  • ...Since then MC method has been used to model a large variety of problems, from pi estimation to Schrödinger equation eigenvalues approximation and immune system simulation Dasgupta [11], Mannion and al [12]....

    [...]

Book ChapterDOI
01 Jan 2004
TL;DR: This chapter provides a brief introduction to the biology of theimmune system and describes several approaches used in mathematical modelling of the immune system.
Abstract: The immune system is the natural defense of an organism. It comprises a network of cells, molecules, and organs whose primary tasks are to defend the organism from pathogens and maintain its integrity. The cooperation between the components of the immune system network realizes effectively and efficiently the processes of pattern recognition, learning, and memory. Our knowledge of the immune system is still incomplete and mathematical modelling has been shown to help better understanding of its underlying principles and organization. In this chapter we provide a brief introduction to the biology of the immune system and describe several approaches used in mathematical modelling of the immune system.

12 citations

References
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Journal ArticleDOI
TL;DR: A large-scale model of the immune network is analyzed, using the shape-space formalism, and how the repertoire organizes itself into large clusters of clones having similar behavior is described.

114 citations

Journal ArticleDOI
TL;DR: This work simulates a d-dimensional hypercubic lattice with dimension d up to 10 and checks for the concentration of B cells and the stability against localized perturbations (“damage spreading”).
Abstract: For the reactions among the antibodies of the immune system, fighting against a foreign antigen, the recent model of de Boer, Segel and Perelson introduced a cellular automata approximation for the interaction of different types of B cells (bone marrow derived lymphocytes) In contrast to most physics models, here each lattice site interacts mainly with its mirror image (with respect to the lattice center) in the opposite part of the lattice We simplify their model and then generalize it to include more than one or two shape-space parameters Thus instead of simulating a chain or square lattice, we simulate a d -dimensional hypercubic lattice with dimension d up to 10 In particular, we check for the concentration of B cells and the stability against localized perturbations (“damage spreading”)

25 citations