Current Paradigms in Immunology

  • Eugenio Cesana
  • Silvia Beltrami
  • Antonia Emanuela Laface
  • Astrid Urthaler
  • Alessandra Folci
  • Alberto Clivio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)


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.


Major Histocompatibility Complex Adaptive Immune System Artificial Immune System Major Histocompatibility Complex Molecule Immunological Synapse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eugenio Cesana
    • 1
  • Silvia Beltrami
    • 1
  • Antonia Emanuela Laface
    • 1
  • Astrid Urthaler
    • 1
  • Alessandra Folci
    • 1
  • Alberto Clivio
    • 1
  1. 1.Department of Preclinical Sciences, Faculty of MedicineUniversity of MilanoItaly

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