<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet href="/rss.css" type="text/css"?>
<rdf:RDF xmlns="http://purl.org/rss/1.0/"
    xmlns:cc="http://web.resource.org/cc/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:extra="http://www.w3.org/1999/xhtml"
    xmlns:prism="http://prismstandard.org/namespaces/1.2/basic/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <channel rdf:about="http://immunome-research.com/feeds/latestarticles/journal?quantity=&amp;format=rss&amp;version=">
        <title>Immunome Research - Latest Articles</title>
        <link>http://www.immunome-research.com</link>
        <description>The latest research articles published by Immunome Research</description>
        <dc:date>2010-02-18T00:00:00Z</dc:date>
        <items>
            <rdf:Seq>
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/6/1/1" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/5/1/5" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/5/1/4" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/5/1/3" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/5/1/2" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/5/1/1" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/4/1/7" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/4/1/6" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/4/1/5" />
                                <rdf:li rdf:resource="http://www.immunome-research.com/content/4/1/4" />
                            </rdf:Seq>
        </items>
        <extra:info rdf:parseType="Literal">
            <html:div style="font:14px Verdana, Geneva, Arial, Helvetica, sans-serif" xmlns:html="http://www.w3.org/1999/xhtml">
                <html:span style="font-weight:bold">
                    This is an RSS newsfeed from BioMed Central
                </html:span>
                <html:br />
                <html:span style="font-size: 12px;">
                    It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit
                    <html:br />
                    <html:a href="http://www.biomedcentral.com/info/about/rss/" style="color:#3333CC; font-size:12px;">
                        http://www.biomedcentral.com/info/about/rss/
                    </html:a>
                    <html:br />
                </html:span>
            </html:div>
        </extra:info>
        <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </channel>
        <item rdf:about="http://www.immunome-research.com/content/6/1/1">
        <title>A novel paradigm for cell and molecule interaction ontology:
from the CMM model to IMGT-ONTOLOGY</title>
        <description>Background:
Biology is moving fast toward the virtuous circle of other disciplines: from data to quantitative modeling and back to data. Models are usually developed by mathematicians, physicists, and computer scientists to translate qualitative or semi-quantitative biological knowledge into a quantitative approach. To eliminate semantic confusion between biology and other disciplines, it is necessary to have a list of the most important and frequently used concepts coherently defined.
Results:
We propose a novel paradigm for generating new concepts for an ontology, starting from model rather than developing a database. We apply that approach to generate concepts for cell and molecule interaction starting from an agent based model. This effort provides a solid infrastructure that is useful to overcome the semantic ambiguities that arise between biologists and mathematicians, physicists, and computer scientists, when they interact in a multidisciplinary field.
Conclusions:
This effort represents the first attempt at linking molecule ontology with cell ontology, in IMGT-ONTOLOGY, the well established ontology in immunogenetics and immunoinformatics, and a paradigm for life science biology. With the increasing use of models in biology and medicine, the need to link different levels, from molecules to cells to tissues and organs, is increasingly important.</description>
        <link>http://www.immunome-research.com/content/6/1/1</link>
                <dc:creator>Francesco Pappalardo</dc:creator>
                <dc:creator>Marie-Paule Lefranc</dc:creator>
                <dc:creator>Pier-Luigi Lollini</dc:creator>
                <dc:creator>Santo Motta</dc:creator>
                <dc:source>Immunome Research 2010, 6:1</dc:source>
        <dc:date>2010-02-18T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-6-1</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>6</prism:volume>
        <prism:startingPage>1</prism:startingPage>
        <prism:publicationDate>2010-02-18T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/5/1/5">
        <title>Ribosomal protein mRNAs are translationally-regulated 
during human dendritic cells activation by LPS
</title>
        <description>Background:
Dendritic cells (DCs) are the sentinels of the mammalian immune system, characterized by a complex maturation process driven by pathogen detection. Although multiple studies have described the analysis of activated DCs by transcriptional profiling, recent findings indicate that mRNAs are also regulated at the translational level. A systematic analysis of the mRNAs being translationally regulated at various stages of DC activation was performed using translational profiling, which combines sucrose gradient fractionation of polysomal-bound mRNAs with DNA microarray analysis.
Results:
Total and polysomal-bound mRNA populations purified from immature, 4 h and 16 h LPS-stimulated human monocyte-derived DCs were analyzed on Affymetrix microarrays U133 2.0. A group of 375 transcripts was identified as translationally regulated during DC-activation. In addition to several biochemical pathways related to immunity, the most statistically relevant biological function identified among the translationally regulated mRNAs was protein biosynthesis itself. We singled-out a cluster of 11 large ribosome proteins mRNAs, which are disengaged from polysomes at late time of maturation, suggesting the existence of a negative feedback loop regulating translation in DCs and linking ribosomal proteins to immuno-modulatory function.
Conclusion:
Our observations highlight the importance of translation regulation during the immune response, and may favor the identification of novel protein networks relevant for immunity. Our study also provides information on the potential absence of correlation between gene expression and protein production for specific mRNA molecules present in DCs.</description>
        <link>http://www.immunome-research.com/content/5/1/5</link>
                <dc:creator>Maurizio Ceppi</dc:creator>
                <dc:creator>Giovanna Clavarino</dc:creator>
                <dc:creator>Evelina Gatti</dc:creator>
                <dc:creator>Enrico Schmidt</dc:creator>
                <dc:creator>Aude de Gassart</dc:creator>
                <dc:creator>Derek Blankenship</dc:creator>
                <dc:creator>Gerald Ogola</dc:creator>
                <dc:creator>Jacques Banchereau</dc:creator>
                <dc:creator>Damien Chaussabel</dc:creator>
                <dc:creator>Philippe Pierre</dc:creator>
                <dc:source>Immunome Research 2009, 5:5</dc:source>
        <dc:date>2009-11-27T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-5-5</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>5</prism:volume>
        <prism:startingPage>5</prism:startingPage>
        <prism:publicationDate>2009-11-27T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/5/1/4">
        <title>Critical role of glycosylation in determining the length and structure of T cell epitopes
- As suggested by a combined in silico systems biology approach

</title>
        <description>Background:
Using a combined in silico approach, we investigated the glycosylation of T cell epitopes and autoantigens. The present systems biology analysis was made possible by currently available databases (representing full proteomes, known human T cell epitopes and autoantigens) as well as glycosylation prediction tools.
Results:
We analyzed the probable glycosylation of human T cell epitope sequences extracted from the ImmuneEpitope Database. Our analysis suggests that in contrast to full length SwissProt entries, only a minimal portion of experimentally verified T cell epitopes is potentially N- or O-glycosylated (2.26% and 1.22%, respectively). Bayesian analysis of entries extracted from the Autoantigen Database suggests a correlation between N-glycosylation and autoantigenicity. The analysis of random generated sequences shows that glycosylation probability is also affected by peptide length. Our data suggest that the lack of peptide glycosylation, a feature that probably favors effective recognition by T cells, might have resulted in a selective advantage for short peptides to become T cell epitopes. The length of T cell epitopes is at the intersection of curves determining specificity and glycosylation probability. Thus, the range of length of naturally occurring T cell epitopes may ensure the maximum specificity with the minimal glycosylation probability.
Conclusion:
The findings of this bioinformatical approach shed light on fundamental factors that might have shaped adaptive immunity during evolution. Our data suggest that amino acid sequence-based hypo/non-glycosylation of certain segments of proteins might be substantial for determining T cell immunity/autoimmunity.</description>
        <link>http://www.immunome-research.com/content/5/1/4</link>
                <dc:creator>Tamas Szabo</dc:creator>
                <dc:creator>Robin Palotai</dc:creator>
                <dc:creator>Peter Antal</dc:creator>
                <dc:creator>Itay Tokatly</dc:creator>
                <dc:creator>Laszlo Tothfalusi</dc:creator>
                <dc:creator>Ole Lund</dc:creator>
                <dc:creator>Gyorgy Nagy</dc:creator>
                <dc:creator>Andras Falus</dc:creator>
                <dc:creator>Edit Buzas</dc:creator>
                <dc:source>Immunome Research 2009, 5:4</dc:source>
        <dc:date>2009-09-24T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-5-4</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>5</prism:volume>
        <prism:startingPage>4</prism:startingPage>
        <prism:publicationDate>2009-09-24T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/5/1/3">
        <title>Of mice and humans: how good are HLA transgenic mice as a model of human immune responses?</title>
        <description>Background:
Previous studies have defined vaccinia virus (VACV)-derived T cell epitopes in VACV-infected human leukocyte antigen-A*0201 (HLA-A2.1) transgenic (Tg) mice and A2.1-positive human Dryvax vaccinees. A total of 14 epitopes were detected in humans and 16 epitopes in A2.1 Tg mice; however, only two epitopes were independently reported in both systems. This limited overlap raised questions about the suitability of using HLA Tg mice as a model system to map human T cell responses to a complex viral pathogen. The present study was designed to investigate this issue in more detail.
Results:
Re-screening the panel of 28 A2.1-restricted epitopes in additional human vaccinees and in A2.1 Tg mice revealed that out of the 28 identified epitopes, 13 were detectable in both systems, corresponding to a 46% concordance rate. Interestingly, the magnitude of responses in Tg mice against epitopes originally identified in humans is lower than for epitopes originally detected in mice. Likewise, responses in humans against epitopes originally detected in Tg mice are of lower magnitude.
Conclusion:
These data suggest that differences in immunodominance patterns might explain the incomplete response overlap, and that with limitations; HLA Tg mice represent a relevant and suitable model system to study immune responses against complex pathogens.</description>
        <link>http://www.immunome-research.com/content/5/1/3</link>
                <dc:creator>Maya Kotturi</dc:creator>
                <dc:creator>Erika Assarsson</dc:creator>
                <dc:creator>Bjoern Peters</dc:creator>
                <dc:creator>Howard Grey</dc:creator>
                <dc:creator>Carla Oseroff</dc:creator>
                <dc:creator>Valerie Pasquetto</dc:creator>
                <dc:creator>Alessandro Sette</dc:creator>
                <dc:source>Immunome Research 2009, 5:3</dc:source>
        <dc:date>2009-06-17T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-5-3</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>5</prism:volume>
        <prism:startingPage>3</prism:startingPage>
        <prism:publicationDate>2009-06-17T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/5/1/2">
        <title>Functional recombinant MHC class II molecules and high-throughput peptide-binding assays</title>
        <description>Background:
Molecules of the class II major histocompability complex (MHC-II) specifically bind and present exogenously derived peptide epitopes to CD4+ T helper cells. The extreme polymorphism of the MHC-II hampers the complete analysis of peptide binding. It is also a significant hurdle in the generation of MHC-II molecules as reagents to study and manipulate specific T helper cell responses. Methods to generate functional MHC-II molecules recombinantly, and measure their interaction with peptides, would be highly desirable; however, no consensus methodology has yet emerged.
Results:
We generated &#945; and &#946; MHC-II chain constructs, where the membrane-spanning regions were replaced by dimerization motifs, and the C-terminal of the &#946; chains was fused to a biotinylation signal peptide (BSP) allowing for in vivo biotinylation. These chains were produced separately as inclusion bodies in E. coli , extracted into urea, and purified under denaturing and non-reducing conditions using conventional column chromatography. Subsequently, diluting the two chains into a folding reaction with appropriate peptide resulted in efficient peptide-MHC-II complex formation. Several different formats of peptide-binding assay were developed including a homogeneous, non-radioactive, high-throughput (HTS) binding assay. Binding isotherms were generated allowing the affinities of interaction to be determined. The affinities of the best binders were found to be in the low nanomolar range. Recombinant MHC-II molecules and accompanying HTS peptide-binding assay were successfully developed for nine different MHC-II molecules including the DPA1*0103/DPB1*0401 (DP401) and DQA1*0501/DQB1*0201, where both &#945; and &#946; chains are polymorphic, illustrating the advantages of producing the two chains separately.
Conclusion:
We have successfully developed versatile MHC-II resources, which may assist in the generation of MHC class II -wide reagents, data, and tools.</description>
        <link>http://www.immunome-research.com/content/5/1/2</link>
                <dc:creator>Sune Justesen</dc:creator>
                <dc:creator>Mikkel Harndahl</dc:creator>
                <dc:creator>Kasper Lamberth</dc:creator>
                <dc:creator>Lise-Lotte Nielsen</dc:creator>
                <dc:creator>Soren Buus:</dc:creator>
                <dc:source>Immunome Research 2009, 5:2</dc:source>
        <dc:date>2009-05-05T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-5-2</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>5</prism:volume>
        <prism:startingPage>2</prism:startingPage>
        <prism:publicationDate>2009-05-05T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/5/1/1">
        <title>Genetic correlates of autoreactivity and autoreactive potential in human Ig heavy chains</title>
        <description>Background:
Immature bone marrow B cells are known to have longer CDR3 than mature peripheral B cells, and this genetic characteristic has been shown to correlate with autoreactivity in these early cells. B-cell Central tolerance eliminates these cells, but it is known that autoreactive B cells nevertheless appear commonly in healthy human blood. We examined over 7,300 Ig genes from Genbank, including those annotated by their discoverers as associated with autoreactivity, to determine the genetic correlates of autoreactivity in mature B cells.
Results:
We find differential biases in gene segment usage and higher mutation frequency in autoreactivity-associated Ig genes, but the CDR3 lengths do not differ between autoreactive and non-autoreactive Ig genes. The most striking genetic signature of autoreactivity is an increase in the proportion of N-nucleotides relative to germline-encoded nucleotides in CDR3 from autoreactive genes.
Conclusion:
We hypothesize that peripheral autoreactivity results primarily from somatic mutation, and that the genetic correlates of autoreactivity in mature B-cells are not the same as those for autoreactivity in immature B cells. What is seen in mature autoreactive B cells are the correlates of autoreactive potential, not of autoreactivity per se. The autoreactive potential is higher for V(D)J rearrangements encoded to a large extent by N-nucleotides rather than by the gene segments that, we posit, have been selected in germline evolution for their suppression of autoreactive potential.</description>
        <link>http://www.immunome-research.com/content/5/1/1</link>
                <dc:creator>Joseph Volpe</dc:creator>
                <dc:creator>Thomas Kepler</dc:creator>
                <dc:source>Immunome Research 2009, 5:1</dc:source>
        <dc:date>2009-02-27T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-5-1</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>5</prism:volume>
        <prism:startingPage>1</prism:startingPage>
        <prism:publicationDate>2009-02-27T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/4/1/7">
        <title>Data mining of cancer vaccine trials: a bird&apos;s-eye view</title>
        <description>Background:
A wealth of information on clinical trials has been provided by publicly accessible online registries. Information technology and data exchange standards enable rapid extraction, summarization, and visualization of information and derived knowledge from these data sets. Clinical trials data was extracted in the XML format from the National Library of Medicine ClinicalTrials.gov site. This data includes categories such as &apos;Summary of Purpose&apos;, &apos;Trial Sponsor&apos;, &apos;Phase of the Trial&apos;, &apos;Recruiting Status&apos;, and &apos;Location&apos;. We focused on 645 clinical trials related to cancer vaccines. Additional facts on cancer types, including incidence and survival rates, were retrieved from the National Cancer Institute Surveillance data.
Results:
This application enables rapid extraction of information about institutions, diseases, clinical approaches, clinical trials dates, predominant cancer types in the trials, clinical opportunities and pharmaceutical market coverage. Presentation of results is facilitated by visualization tools that summarize the landscape of ongoing and completed cancer vaccine trials. Our summaries show the number of clinical vaccine trials per cancer type, over time, by phase, by lead sponsors, as well as trial activity relative to cancer type and survival data. We also have identified cancers that are neglected in the cancer vaccine field: bladder, liver, pancreatic, stomach, esophageal, and all of the low-incidence cancers.
Conclusion:
We have developed a data mining approach that enables rapid extraction of complex data from the major clinical trial repository. Summarization and visualization of these data represents a cost-effective means of making informed decisions about future cancer vaccine clinical trials.</description>
        <link>http://www.immunome-research.com/content/4/1/7</link>
                <dc:creator>Xiaohong Cao</dc:creator>
                <dc:creator>Karen Maloney</dc:creator>
                <dc:creator>Vladimir Brusic</dc:creator>
                <dc:source>Immunome Research 2008, 4:7</dc:source>
        <dc:date>2008-12-12T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-4-7</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>4</prism:volume>
        <prism:startingPage>7</prism:startingPage>
        <prism:publicationDate>2008-12-12T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/4/1/6">
        <title>Peptide length significantly influences in vitro affinity for MHC class II molecules</title>
        <description>Background:
Class II Major Histocompatibility Complex (MHC) molecules have an open-ended binding groove which can accommodate peptides of varying lengths. Several studies have demonstrated that peptide flanking residues (PFRs) which lie outside the core binding groove can influence peptide binding and T cell recognition. By using data from the AntiJen database we were able to characterise systematically the influence of PFRs on peptide affinity for MHC class II molecules.
Results:
By analysing 1279 peptide elongation events covering 19 distinct HLA alleles it was observed that, in general, peptide elongation resulted in increased MHC class II molecule affinity. It was also possible to determine an optimal peptide length for MHC class II affinity of approximately 18&#8211;20 amino acids; elongation of peptides beyond this length resulted in a null or negative effect on affinity.
Conclusion:
The observed relationship between peptide length and MHC class II affinity has significant implications for the design of vaccines and the study of the epitopic basis of immunological disease.</description>
        <link>http://www.immunome-research.com/content/4/1/6</link>
                <dc:creator>Cathal O'Brien</dc:creator>
                <dc:creator>Darren Flower</dc:creator>
                <dc:creator>Conleth Feighery</dc:creator>
                <dc:source>Immunome Research 2008, 4:6</dc:source>
        <dc:date>2008-11-26T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-4-6</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>4</prism:volume>
        <prism:startingPage>6</prism:startingPage>
        <prism:publicationDate>2008-11-26T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/4/1/5">
        <title>Expression Analysis of G Protein-Coupled Receptors in Mouse Macrophages</title>
        <description>Background:
Monocytes and macrophages express an extensive repertoire of G Protein-Coupled Receptors (GPCRs) that regulate inflammation and immunity. In this study we performed a systematic micro-array analysis of GPCR expression in primary mouse macrophages to identify family members that are either enriched in macrophages compared to a panel of other cell types, or are regulated by an inflammatory stimulus, the bacterial product lipopolysaccharide (LPS).
Results:
Several members of the P2RY family had striking expression patterns in macrophages; P2ry6 mRNA was essentially expressed in a macrophage-specific fashion, whilst P2ry1 and P2ry5 mRNA levels were strongly down-regulated by LPS. Expression of several other GPCRs was either restricted to macrophages (e.g. Gpr84) or to both macrophages and neural tissues (e.g. P2ry12, Gpr85). The GPCR repertoire expressed by bone marrow-derived macrophages and thioglycollate-elicited peritoneal macrophages had some commonality, but there were also several GPCRs preferentially expressed by either cell population.
Conclusion:
The constitutive or regulated expression in macrophages of several GPCRs identified in this study has not previously been described. Future studies on such GPCRs and their agonists are likely to provide important insights into macrophage biology, as well as novel inflammatory pathways that could be future targets for drug discovery.</description>
        <link>http://www.immunome-research.com/content/4/1/5</link>
                <dc:creator>Jane Lattin</dc:creator>
                <dc:creator>Kate Schroder</dc:creator>
                <dc:creator>Andrew Su</dc:creator>
                <dc:creator>John Walker</dc:creator>
                <dc:creator>Jie Zhang</dc:creator>
                <dc:creator>Tim Wiltshire</dc:creator>
                <dc:creator>Kaoru Saijo</dc:creator>
                <dc:creator>Christopher Glass</dc:creator>
                <dc:creator>David Hume</dc:creator>
                <dc:creator>Stuart Kellie</dc:creator>
                <dc:creator>Matthew Sweet</dc:creator>
                <dc:source>Immunome Research 2008, 4:5</dc:source>
        <dc:date>2008-04-29T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-4-5</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>4</prism:volume>
        <prism:startingPage>5</prism:startingPage>
        <prism:publicationDate>2008-04-29T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <item rdf:about="http://www.immunome-research.com/content/4/1/4">
        <title>Efficiency of the immunome protein interaction network increases during evolution</title>
        <description>Background:
Details of the mechanisms and selection pressures that shape the emergence and development of complex biological systems, such as the human immune system, are poorly understood. A recent definition of a reference set of proteins essential for the human immunome, combined with information about protein interaction networks for these proteins, facilitates evolutionary study of this biological machinery.
Results:
Here, we present a detailed study of the development of the immunome protein interaction network during eight evolutionary steps from Bilateria ancestors to human. New nodes show preferential attachment to high degree proteins. The efficiency of the immunome protein interaction network increases during the evolutionary steps, whereas the vulnerability of the network decreases.
Conclusion:
Our results shed light on selective forces acting on the emergence of biological networks. It is likely that the high efficiency and low vulnerability are intrinsic properties of many biological networks, which arise from the effects of evolutionary processes yet to be uncovered.</description>
        <link>http://www.immunome-research.com/content/4/1/4</link>
                <dc:creator>Csaba Ortutay</dc:creator>
                <dc:creator>Mauno Vihinen</dc:creator>
                <dc:source>Immunome Research 2008, 4:4</dc:source>
        <dc:date>2008-04-22T00:00:00Z</dc:date>
        <dc:identifier>doi:10.1186/1745-7580-4-4</dc:identifier>
        <prism:publicationName>Immunome Research</prism:publicationName>
        <prism:issn>1745-7580</prism:issn>
        <prism:volume>4</prism:volume>
        <prism:startingPage>4</prism:startingPage>
        <prism:publicationDate>2008-04-22T00:00:00Z</prism:publicationDate>
                <prism:versionidentifier>XML</prism:versionidentifier>
                <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
    </item>
        <cc:License rdf:about="http://creativecommons.org/licenses/by/2.0/">
        <cc:permits rdf:resource="http://creativecommons.org/ns#Reproduction" />
        <cc:permits rdf:resource="http://creativecommons.org/ns#Distribution" />
        <cc:permits rdf:resource="http://creativecommons.org/ns#DerivativeWorks" />
    </cc:License>
</rdf:RDF>
