Chaos Nameplat
  • Volume/Page
  • Keyword
  • DOI
  • Citation
  • Advanced
   
 
 
 

You Tube Flickr Twitter iResearch App Facebook

Chaos / Volume 21 / Issue 1 / FOCUS ISSUE: MESOSCALES IN COMPLEX NETWORKS

Chaos 21, 016109 (2011); http://dx.doi.org/10.1063/1.3543800 (11 pages)

Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood

Asaf Madi1,2, Dror Y. Kenett1, Sharron Bransburg-Zabary1,2, Yifat Merbl3, Francisco J. Quintana3, Stefano Boccaletti1,4, Alfred I. Tauber5, Irun R. Cohen3, and Eshel Ben-Jacob1,6

1School of Physics and Astronomy, Tel Aviv University, 69978 Tel Aviv, Israel
2Faculty of Medicine, Tel Aviv University, 69978 Tel Aviv, Israel
3Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel
4CNR, Institute of Complex Systems, Florence, Italy and the Italian Embassy in Israel, Israel
5School of Medicine, Boston University, Boston, Massachusetts 02215, USA
6The Center for Theoretical and Biological Physics, University of California San Diego, La Jolla, California 92093, USA

View MapView Map

(Received 28 October 2010; accepted 29 December 2010; published online 29 March 2011)

Much effort has been devoted to assess the importance of nodes in complex biological networks (such as gene transcriptional regulatory networks, protein interaction networks, and neural networks). Examples of commonly used measures of node importance include node degree, node centrality, and node vulnerability score (the effect of the node deletion on the network efficiency). Here, we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node-node correlations. To this end, we first define the dependency of node i on node j (or the influence of node j on node i), D(i, j) as the average over all nodes k of the difference between the i − k correlation and the partial correlations between these nodes with respect to node j. Note that the dependencies, D(i, j) define a directed weighted matrix, since, in general, D(i, j) differs from D( j, i). For this reason, many of the commonly used measures of node importance, such as node centrality, cannot be used. Hence, to assess the node importance of the dependency networks, we define the system level influence (SLI) of antigen j, SLI( j) as the sum of the influence of j on all other antigens i. Next, we define the system level influence or the influence score of antigen j, SLI( j) as the sum of D(i, j) over all nodes i. We introduce the new approach and demonstrate that it can unveil important biological information in the context of the immune system. More specifically, we investigated antigen dependency networks computed from antigen microarray data of autoantibody reactivity of IgM and IgG isotypes present in the sera of ten mothers and their newborns. We found that the analysis was able to unveil that there is only a subset of antigens that have high influence scores (SLI) common both to the mothers and newborns. Networks comparison in terms of modularity (using the Newman’s algorithm) and of topology (measured by the divergence rate) revealed that, at birth, the IgG networks exhibit a more profound global reorganization while the IgM networks exhibit a more profound local reorganization. During immune system development, the modularity of the IgG network increases and becomes comparable to that of the IgM networks at adulthood. We also found the existence of several conserved IgG and IgM network motifs between the maternal and newborns networks, which might retain network information as our immune system develops. If correct, these findings provide a convincing demonstration of the effectiveness of the new approach to unveil most significant biological information. Whereas we have introduced the new approach within the context of the immune system, it is expected to be effective in the studies of other complex biological social, financial, and manmade networks.

© 2011 American Institute of Physics

Article Outline

  1. INTRODUCTION
  2. METHODS
    1. The antigen–antigen correlations
    2. Partial correlations
    3. The correlation influence and correlation dependency
    4. Antigen dependencies
    5. The antigen SLI
    6. Network representation of the antigen dependencies
    7. Informative subgraphs of the dependency network
    8. Construction of the PMFG informative subgraph
    9. Hybrid presentation of the informative subgraphs
    10. Network comparison based on divergence rate
    11. Network comparison based on network modularity
  3. RESULTS
    1. Separation to isotypes
    2. Modularity of the antigen network
    3. Conserved elements subnetworks
  4. DISCUSSION

RELATED DATABASES

Scopus

View This Record in Scopus

Web of Science

View this record in Web of Science

View Web of Science related articles

KEYWORDS and PACS

Keywords

biochemistry, bioinformatics, cellular biophysics, macromolecules, molecular biophysics

PACS

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3543800

PUBLICATION DATA

ISSN

1054-1500 (print)  
1089-7682 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

For access to fully linked references, you need to log in.

    References

    U. Lee and S. Kim, Phys. Rev. E 73, 041920 (2006).

    S.-G. Lee and S. Kim, Phys. Rev. E 73, 041924 (2006).


View Scopus articles citing this one (3) Scopus Help

Figures (9) Tables (1)

Figures (click on thumbnails to view enlargements)

FIG.1
(Color) The hybrid presentation of the informative subgraphs of the correlationvs dependency network. (a) Maternal dependency network, and (b) maternal correlations. (c) and (d), the same as (a) and (b) for the newborns. To simplify the presentation, we show the subgraphs for a selected subset of only 20 antigens that are the top separators between the two groups of subjects using t-test ranking between the two datasets. Arrows indicate the directionality of the influence. The antigen (nodes) influence scores (SLI) is color coded from dark blue for the least influential antigens to dark red for the most influential ones.

FIG.1 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.2
(Color) The hybrid presentation of the informative subgraphs of the dependency network. The selected layout, Fruchterman–Reingold three-dimensional (3D), visualizes the differences between the maternal (a, c) and the newborns (b, d) networks. In (a, b), the nodes were colored according to the isotypes, IgG: green and IgM: red. In (c, d), the colors indicate the strength of the SLI of each antigen on the correlations between all other antigen pairs, from most affecting antigen (dark red) to least affecting antigen (dark blue), The arrows indicate the directionality of the influence. Note the wide dispersal of highly affecting antigens in the networks.

FIG.2 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.3
(Color online) Top 20 most influential antigens in the dependency networks of the (a) mothers and (b) newborns. The bars indicate the SLI of each antigen on the correlations between all other antigen pairs in the network. For visualization proposes, influence score (SLI) values were rescaled between 0 and 1.

FIG.3 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.4
(Color) The hybrid presentation of the informative subgraphs of the dependency isotypic network presented with the Fruchterman–Reingold 3D layout. (a) Mothers’ IgG, (b) mothers’ IgM, (c) newborns’ IgG, and (d) newborns’ IgM. The colors indicate the strength of the SLI of each antigen on the correlations between all other antigen pairs, from most affecting antigens (dark red) to least affecting antigens (dark blue). The arrows indicate the directionality of the influence.

FIG.4 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.5
(Color) The hybrid presentation of the informative subgraphs of the dependency network for the separate isotypes. The selected layout, Kamada–Kawai, was partitioned by Newman’s algorithm, and each cluster was assigned a different color. (a) Mothers’ IgG, (b) mothers’ IgM, (c) newborns’ IgG, and (d) newborns’ IgM networks. Arrows indicate the directionality of the influence. Note that the same colors between two networks do not indicate similar members.

FIG.5 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.6
(Color) The hybrid presentation of the largest conserved element between the (a) IgG informative subgraphs of mothers and newborns. (b) The same for IgM isotype. Arrows indicate the directionality of the influence.

FIG.6 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.7
(Color) Top 100 most influential antigens in the dependency networks of the (a) mothers, (b) newborns, (c) mothers IgG, (d) newborns IgG, (e) mothers IgM, and (f) newborns IgM. The antigens were ranked according to their SLI scores and divided by the maximum score to get a relative SLI score. Note the change in SLI scores around 20 antigens mainly in the integrated diabetic and healthy IgM networks.

FIG.7 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.8
(Color) The hybrid presentation of the informative subgraphs of the correlation vs dependency network. (a) Maternal IgG correlation, (b) maternal IgG dependency network, (c) maternal IgM correlations, and (d) maternal IgM dependency network. (a, c) The selected layout, Kamada–Kawai, for the correlation based PMFG was partitioned by Newman’s algorithm, and each cluster was assigned a different color. (b, d) Superimposing the cluster coloring on the dependency networks. Arrows indicate the directionality of the influence. Note that the same colors between two networks do not indicate necessarily similar nodes but members of the same cluster.

FIG.8 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.9
(Color) Conserved subnetworks. (a) The IgG conserved subnetworks between the maternal and newborns networks. (b) The same for IgM. (c) The maternal IgG dependency network with all conserved elements marked in red. (d) The same for the IgM network.

FIG.9 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

Tables

Table I. Top 20 most influential antigens in the isotype networks.

View Table


Close

Google Calendar

ADVERTISEMENT

Your Recent History Recent History Help

Recently Viewed

  1. Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood
Go to AIP Home   © AIP Publishing LLC
close