Skip to main content

EDITORIAL article

Front. Immunol., 19 December 2014
Sec. B Cell Biology
This article is part of the Research Topic Immune system modeling and analysis View all 14 articles

Immune System Modeling and Analysis

  • Computational Immunology Lab, The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel

Immunologists currently face daunting challenges, as a result of the rapid development of new methods for immunological data collection, from high-throughput phenotyping to deep sequencing (1). These and similar methods keep generating humongous amounts of immunological data, which in turn challenge the theoretical immunology community to develop methods for data organization and analysis and mathematical and computational modeling. These challenges and methods were discussed in recent workshops, for example the Lymphocyte Repertoire Workshop (Institute of Advanced Studies of the Hebrew University, Jerusalem, early 2012, organized by myself), and the International Seminar on Multi-Scale Physics of Lymphocyte Development (Max Planck Institute for the Physics of Complex Systems, Dresden, Summer 2012, organized by M. Or-Guil et al.).

At about the same time, the organizers mentioned above were approached by the Frontiers editorial staff with the idea for a “Frontiers in Immunology” research topic, which was to provide a comprehensive, online, open access snapshot of the current state of the art on immune system modeling and analysis. The research topic was launched, edited, and finalized with the kind help of co-editors Rob de Boer, Miles Davenport, Carmen Molina-Paris, Michal Or-Guil, and Veronika Zarnitsyna. It has been a success, with 35 papers accepted for publication, which attests to the timeliness of the topic.

The papers included in this Research Topic reflect many of the issues that theoretical immunologists are struggling with. Some of the papers address old questions – such as the targeting of somatic hypermutation (2) and the resulting diversity of B cell repertoires (3, 4), how clonal selection operates in germinal centers (58); or how the T cell compartment develops (911) and changes with aging (12). However, these papers offer new viewpoints, which emerged thanks to the immunological “data revolution”, in particular next-generation sequencing of lymphocyte repertoires. Others address new methods of extracting (1315) and analyzing (1618) comprehensive T and B cell phenotype and repertoire data, and delineate some of the first insights gleaned from sequencing studies regarding how these repertoires emerge, evolve, and function (1925). Natural killer cells (26), myeloid cells (27), and structural immunology (2831) are also represented.

My thanks go to the above-mentioned co-editors, to the responsive and efficient Frontiers editorial staff, to all the authors who contributed papers, and to the reviewers whose work has made publication of all these papers possible.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

1. Mehr R, Sternberg-Simon M, Michaeli M, Pickman Y. Models and methods for analysis of lymphocyte repertoire generation, development, selection and evolution. Immunol Lett (2012) 148:11. doi:10.1016/j.imlet.2012.08.002

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

2. Yaari G, Vander Heiden JA, Uduman M, Gadala-Maria D, Gupta N, Stern JN, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front Immunol (2013) 4:358. doi:10.3389/fimmu.2013.00358

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

3. Jackson KJ, Kidd MJ, Wang Y, Collins AM. The shape of the lymphocyte receptor repertoire: lessons from the B cell receptor. Front Immunol (2013) 4:263. doi:10.3389/fimmu.2013.00263

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

4. Michaeli M, Tabibian-Keissar H, Schiby G, Shahaf G, Pickman Y, Hazanov L, et al. Immunoglobulin gene repertoire diversification and selection in the stomach – from gastritis to gastric lymphomas. Front Immunol (2014) 5:264. doi:10.3389/fimmu.2014.00264

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

5. Schwartz GW, Hershberg U. Germline amino acid diversity in B cell receptors is a good predictor of somatic selection pressures. Front Immunol (2013) 4:357. doi:10.3389/fimmu.2013.00357

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

6. Liberman G, Benichou J, Tsaban L, Glanville J, Louzoun Y. Multi step selection in Ig H chains is initially focused on CDR3 and then on other CDR regions. Front Immunol (2013) 4:274. doi:10.3389/fimmu.2013.00274

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

7. Kepler TB, Munshaw S, Wiehe K, Zhang R, Yu JS, Woods CW, et al. Reconstructing a B-cell clonal lineage. II. Mutation, selection, and affinity maturation. Front Immunol (2014) 5:170. doi:10.3389/fimmu.2014.00170

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

8. Or-Guil M, Faro J. A major hindrance in antibody affinity maturation investigation: we never succeeded in falsifying the hypothesis of single-step selection. Front Immunol (2014) 5:237. doi:10.3389/fimmu.2014.00237

CrossRef Full Text | Google Scholar

9. Yates AJ. Theories and quantification of thymic selection. Front Immunol (2014) 5:13. doi:10.3389/fimmu.2014.00013

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

10. Reynolds J, Coles M, Lythe G, Molina-París C. Mathematical model of naive T cell division and survival IL-7 thresholds. Front Immunol (2013) 4:434. doi:10.3389/fimmu.2013.00434

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

11. Hapuarachchi T, Lewis J, Callard RE. A mechanistic model for naive CD4 T cell homeostasis in healthy adults and children. Front Immunol (2013) 4:366. doi:10.3389/fimmu.2013.00366

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

12. Shifrut E, Baruch K, Gal H, Ndifon W, Deczkowska A, Schwartz M, et al. CD4+ T cell-receptor repertoire diversity is compromised in the spleen but not in the bone marrow of aged mice due to private and sporadic clonal expansions. Front Immunol (2013) 4:379. doi:10.3389/fimmu.2013.00379

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

13. Fiala GJ, Kaschek D, Blumenthal B, Reth M, Timmer J, Schamel WW. Pre-clustering of the B cell antigen receptor demonstrated by mathematically extended electron microscopy. Front Immunol (2013) 4:427. doi:10.3389/fimmu.2013.00427

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

14. Mamedov IZ, Britanova OV, Zvyagin IV, Turchaninova MA, Bolotin DA, Putintseva EV, et al. Preparing unbiased T-cell receptor and antibody cDNA libraries for the deep next generation sequencing profiling. Front Immunol (2013) 4:456. doi:10.3389/fimmu.2013.00456

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

15. Chylek LA, Holowka DA, Baird BA, Hlavacek WS. An interaction library for the FcϵRI signaling network. Front Immunol (2014) 5:172. doi:10.3389/fimmu.2014.00172

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

16. Bocharov G, Luzyanina T, Cupovic J, Ludewig B. Asymmetry of cell division in CFSE-based lymphocyte proliferation analysis. Front Immunol (2013) 4:264. doi:10.3389/fimmu.2013.00264

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

17. Thomas-Vaslin V, Six A, Ganascia JG, Bersini H. Dynamical and mechanistic reconstructive approaches of T lymphocyte dynamics: using visual modeling languages to bridge the gap between immunologists, theoreticians, and programmers. Front Immunol (2013) 4:300. doi:10.3389/fimmu.2013.00300

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

18. Zarnitsyna VI, Evavold BD, Schoettle LN, Blattman JN, Antia R. Estimating the diversity, completeness, and cross-reactivity of the T cell repertoire. Front Immunol (2013) 4:485. doi:10.3389/fimmu.2013.00485

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

19. Collins AM, Jackson KJ. A temporal model of human IgE and IgG antibody function. Front Immunol (2013) 4:235. doi:10.3389/fimmu.2013.00235

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

20. Gong C, Linderman JJ, Kirschner D. Harnessing the heterogeneity of T cell differentiation fate to fine-tune generation of effector and memory T cells. Front Immunol (2014) 5:57. doi:10.3389/fimmu.2014.00057

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

21. Six A, Mariotti-Ferrandiz ME, Chaara W, Magadan S, Pham HP, Lefranc MP, et al. The past, present, and future of immune repertoire biology – the rise of next-generation repertoire analysis. Front Immunol (2013) 4:413. doi:10.3389/fimmu.2013.00413

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

22. León K, García-Martínez K, Carmenate T. Mathematical models of the impact of IL2 modulation therapies on T cell dynamics. Front Immunol (2013) 4:439. doi:10.3389/fimmu.2013.00439

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

23. Caridade M, Graca L, Ribeiro RM. Mechanisms underlying CD4+ Treg immune regulation in the adult: from experiments to models. Front Immunol (2013) 4:378. doi:10.3389/fimmu.2013.00378

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

24. Gerdes S, Newrzela S, Glauche I, von Laer D, Hansmann ML, Roeder I. Mathematical modeling of oncogenesis control in mature T-cell populations. Front Immunol (2013) 4:380. doi:10.3389/fimmu.2013.00380

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

25. Kessinger TA, Perelson AS, Neher RA. Inferring HIV escape rates from multi-locus genotype data. Front Immunol (2013) 4:252. doi:10.3389/fimmu.2013.00252

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

26. Carrillo-Bustamante P, Kesmir C, de Boer RJ. Quantifying the protection of activating and inhibiting NK cell receptors during infection with a CMV-like virus. Front Immunol (2014) 5:20. doi:10.3389/fimmu.2014.00020

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

27. Alagha A, Zaikin A. Asymmetry in erythroid-myeloid differentiation switch and the role of timing in a binary cell-fate decision. Front Immunol (2013) 4:426. doi:10.3389/fimmu.2013.00426

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

28. Sela-Culang I, Kunik V, Ofran Y. The structural basis of antibody-antigen recognition. Front Immunol (2013) 4:302. doi:10.3389/fimmu.2013.00302

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

29. Sun J, Kudahl UJ, Simon C, Cao Z, Reinherz EL, Brusic V. Large-scale analysis of B-cell epitopes on influenza virus hemagglutinin – implications for cross-reactivity of neutralizing antibodies. Front Immunol (2014) 5:38. doi:10.3389/fimmu.2014.00038

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

30. Rao X, De Boer RJ, van Baarle D, Maiers M, Kesmir C. Complementarity of binding motifs is a general property of HLA-A and HLA-B molecules and does not seem to effect HLA haplotype composition. Front Immunol (2013) 4:374. doi:10.3389/fimmu.2013.00374

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

31. Castro M, van Santen HM, Férez M, Alarcón B, Lythe G, Molina-París C. Receptor pre-clustering and T cell responses: insights into molecular mechanisms. Front Immunol (2014) 5:132. doi:10.3389/fimmu.2014.00132

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

Keywords: immune system, mathematical modeling, lymphocytes, repertoire, immunomics

Citation: Mehr R (2014) Immune system modeling and analysis. Front. Immunol. 5:644. doi: 10.3389/fimmu.2014.00644

Received: 02 September 2014; Accepted: 03 December 2014;
Published online: 19 December 2014.

Edited and reviewed by: Thomas L. Rothstein, The Feinstein Institute for Medical Research, USA

Copyright: © 2014 Mehr. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: ramit.mehr@biu.ac.il

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.