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Current Paradigms in Immunology

  • Conference paper
Neural Nets (WIRN 2005, NAIS 2005)

Abstract

The last decade has seen a revolution in the field of Immunology. Starting from simple views on the ability of the immune system to respond to foreign antigens or to perform self/not-self discrimination, the image has become much more complex, with the realisation that autoreactive lymphocytes normally circulate in the body, without causing harm to the organism. In fact, the critical point in the development of an immune response is the activation of lymphocytes. This depends on the functional state of antigen-presenting cells and on structural features of the so-called “immune synapse”. Self/not-self discrimination is therefore not as strict as previously thought: on the contrary, it has been shown that a certain degree of self-reactivity is useful, if not necessary, to the homeostasis of the organism. Furthermore, the immune system can be viewed as a network of elements which try to connect with each other to avoid death, and are endowed with emerging properties. In this review, we will make a quick summary of the “classical” paradigms in Immunology, and will discuss the dogmas (specificity, self/not-self discrimination, tolerance) as well as the new ideas to explain how the immune system works, all of them emerging from experimental observations made in the last decade of immunological research. All this may have interesting consequences both for immunologists wanting to make mathematical models of the Immune System and for those involved in the use of immune algorithms for the development of “Artificial Immune Systems” and computational applications.

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© 2006 Springer-Verlag Berlin Heidelberg

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Cesana, E., Beltrami, S., Laface, A.E., Urthaler, A., Folci, A., Clivio, A. (2006). Current Paradigms in Immunology. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_32

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  • DOI: https://doi.org/10.1007/11731177_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33183-4

  • Online ISBN: 978-3-540-33184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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