http://www.cnr.it/ontology/cnr/individuo/prodotto/ID8377
Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system (Articolo in rivista)
- Type
- Label
- Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1371/journal.pone.0009862 (literal)
- Alternative label
Rapin N., Lund L., Bernaschi M. and Castiglione F. (2010)
Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system
in PloS one
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Rapin N., Lund L., Bernaschi M. and Castiglione F. (literal)
- Pagina inizio
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- Scopu (literal)
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1 Biotech Research and Innovation Centre and Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark, 2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark, 3 Institute for Computing Applications, National Research Council, Rome, Italy (literal)
- Titolo
- Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system (literal)
- Abstract
- We present a new approach to the study of the immune system that combines techniques of systems biology with
information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the
immune response, C-IMMSIM, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino
acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the
simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the
immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to
trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods.
In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for
assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a
classical immunization experiment that reproduces the development of immune memory. We also investigate the role of
major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus
and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating
clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity
clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and
consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation
of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the
immune system. (literal)
- Prodotto di
- Autore CNR
Incoming links:
- Autore CNR di
- Prodotto
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi