Cancer vaccines: state of the art of the computational modeling approaches.

Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact among them resulting in growth, invasion, and metastases. The immune system includes many cells and...

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Publicado en:BioMed Research International Vol. 2013; pp. 106407 - 106408
Autores principales: Pappalardo, Francesco, Chiacchio, Ferdinando, Motta, Santo
Formato: Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        atl: Cancer vaccines: state of the art of the computational modeling approaches.
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        au:
          Pappalardo, Francesco
          Chiacchio, Ferdinando
          Motta, Santo
        affil: Dipartimento di Scienze del Farmaco, Università degli Studi di Catania, V.le A. Doria 6, 95125 Catania, Italy. francesco@dmi.unict.it
      sug:
        subj:
          Antigens, Tumor Immunology
          Cancer Vaccines Immunology
          Computer Simulation
          Models, Biological
          Animals
      ab: Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact among them resulting in growth, invasion, and metastases. The immune system includes many cells and molecules that cooperatively act to protect the host organism from foreign agents. Interactions between the immune system and the tumor mass include a huge number of biological factors. Testing of some cancer vaccine features, such as the best conditions for vaccine administration or the identification of candidate antigenic stimuli, can be very difficult or even impossible only through experiments with biological models simply because a high number of variables need to be considered at the same time. This is where computational models, and, to this extent, immunoinformatics, can prove handy as they have shown to be able to reproduce enough biological complexity to be of use in suggesting new experiments. Indeed, computational models can be used in addition to biological models. We now experience that biologists and medical doctors are progressively convinced that modeling can be of great help in understanding experimental results and planning new experiments. This will boost this research in the future.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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