Evidence map›Paper›PMID 20419125›Full record

ArticlePloS one2010

Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system.

Nicolas Rapin, Ole Lund, Massimo Bernaschi, Filippo Castiglione

Abstract read
In one paragraph

Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 609 papers.

0numbers the graph read from it
0cells of the map it votes in
609citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

609 citing papers in PubMed.

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  7. Pangenome-GuidedPharmaceuticals (Basel, Switzerland) · 2026
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549 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nicolas RapinBiotech Research and Innovation Centre and Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark.
Ole Lund
Massimo Bernaschi
Filippo Castiglione

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computer SimulationImmune SystemModels, BiologicalAllergy and ImmunologyComputational BiologyEpitopes, B-LymphocyteEpitopes, T-LymphocyteImmunologic TechniquesSystems BiologyEpitopes, B-LymphocyteEpitopes, T-Lymphocyte

Identifiers

PMID20419125
PMCPMC2855701

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.