Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Bio-modeling Using Petri Nets: A Computational Approach Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Systems Immunology

Authors : Melissa Lever, Thiago C. Hirata, Pedro S. T. Russo, Helder I. Nakaya

Published in: Theoretical and Applied Aspects of Systems Biology

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The advance of systems biology approaches now means that much of the immune response to pathogens and vaccines can be assessed. Modern immunologists have at their disposal an arsenal of high-throughput technologies and tools that generate data relating the quantities of genes, metabolites and proteins within immune cells. The challenge posed is how to interpret this abundance of data to accurately understand and predict the immune response. Systems immunology is the discipline that uses computational and mathematical approaches to integrate these measurements and explain the nonintuitive interactions between biological components. In this chapter we will provide an overview of this interdisciplinary approach, its challenges, and highlight some of the applications of systems biology to assess the complexity of our immune system.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Using Thermodynamic Functions as an Organizing Principle in Cancer Biology
next chapter The Challenge of Translating System Biology into Targeted Therapy of Cancer
Literature
1.
Iwasaki A, Medzhitov R. Control of adaptive immunity by the innate immune system. Nat Immunol. 2015;16:343–53.CrossRef
2.
Chaplin DD. Overview of the immune response. J Allergy Clin Immunol. 2010;125:S3–23.CrossRef
3.
Smith-Garvin JE, Koretzky GA, Jordan MS. T cell activation. Annu Rev Immunol. 2009;27:591–619.CrossRef
4.
Metz PJ, Arsenio J, Kakaradov B, Kim SH, Remedios KA, Oakley K, et al. Regulation of asymmetric division and CD8+ T lymphocyte fate specification by protein kinase Cζ and protein kinase Cλ/ι. J Immunol. 2015;194:2249–59.CrossRef
5.
von Andrian UH, Mempel TR. Homing and cellular traffic in lymph nodes. Nat Rev Immunol. 2003;3:867–78.CrossRef
6.
Krummel MF, Bartumeus F, Gérard A. T cell migration, search strategies and mechanisms. Nat Rev Immunol. 2016;16:193–201.CrossRef
7.
Gonzalez SF, Degn SE, Pitcher LA, Woodruff M, Heesters BA, Carroll MC. Trafficking of B cell antigen in lymph nodes. Annu Rev Immunol. 2011;29:215–33.CrossRef
8.
Hoffman W, Lakkis FG, Chalasani G. B cells, antibodies, and more. Clin J Am Soc Nephrol. 2016;11:137–54.CrossRef
9.
Allman D, Pillai S. Peripheral B cell subsets. Curr Opin Immunol. 2008;20:149–57.CrossRef
10.
Golubovskaya V, Wu L. Different subsets of T cells, memory, effector functions, and CAR-T immunotherapy. Cancers (Basel). 2016;8:E36.CrossRef
11.
Vidal M, Cusick ME, Barabási A-L. Interactome networks and human disease. Cell. 2011;144:986–98.CrossRef
12.
Goldberg AD, Allis CD, Bernstein E. Epigenetics: a landscape takes shape. Cell. 2007;128:635–8.CrossRef
13.
Cech TR, Steitz JA. The noncoding RNA revolution-trashing old rules to forge new ones. Cell. 2014;157:77–94.CrossRef
14.
de Sousa AR, Penalva LO, Marcotte EM, Vogel C. Global signatures of protein and mRNA expression levels. Mol BioSyst. 2009;5:1512–26.
15.
Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol Syst Biol. 2013;9:640.CrossRef
16.
Elhanati Y, Murugan A, Callan CG, Mora T, Walczak AM. Quantifying selection in immune receptor repertoires. Proc Natl Acad Sci U S A. 2014;111:9875–80.CrossRef
17.
Roy B, Neumann RS, Snir O, Iversen R, Sandve GK, Lundin KEA, et al. High-throughput single-cell analysis of B cell receptor usage among autoantigen-specific plasma cells in celiac disease. J Immunol. 2017;199:782–91.CrossRef
18.
19.
Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63.CrossRef
20.
Martinez FO, Gordon S, Locati M, Mantovani A. Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression. J Immunol. 2006;177:7303–11.CrossRef
21.
Zhang JA, Mortazavi A, Williams BA, Wold BJ, Rothenberg EV. Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity. Cell. 2012;149:467–82.CrossRef
22.
Watchmaker PB, Lahl K, Lee M, Baumjohann D, Morton J, Kim SJ, et al. Comparative transcriptional and functional profiling defines conserved programs of intestinal DC differentiation in humans and mice. Nat Immunol. 2014;15:98–108.CrossRef
23.
Isoda T, Moore AJ, He Z, Chandra V, Aida M, Denholtz M, et al. Non-coding transcription instructs chromatin folding and compartmentalization to dictate enhancer-promoter communication and T cell fate. Cell. 2017;171:103–19. e18CrossRef
24.
Wu H, Neilson JR, Kumar P, Manocha M, Shankar P, Sharp PA, et al. miRNA profiling of naïve, effector and memory CD8 T cells. PLoS One. 2007;2:e1020.CrossRef
25.
Gutiérrez-Vázquez C, Rodríguez-Galán A, Fernández-Alfara M, Mittelbrunn M, Sánchez-Cabo F, Martínez-Herrera DJ, et al. miRNA profiling during antigen-dependent T cell activation: a role for miR-132-3p. Sci Rep. 2017;7:3508.CrossRef
26.
Lu Y, Liu X, Xie M, Liu M, Ye M, Li M, et al. The NF-κB-responsive long noncoding RNA FIRRE regulates posttranscriptional regulation of inflammatory gene expression through interacting with hnRNPU. J Immunol. 2017;199:3571–82.CrossRef
27.
Rieckmann JC, Geiger R, Hornburg D, Wolf T, Kveler K, Jarrossay D, et al. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat Immunol. 2017;18:583–93.CrossRef
28.
29.
Jung J, Kim SH, Lee HS, Choi GS, Jung YS, Ryu DH, et al. Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis. Clin Exp Allergy. 2013;43:425–33.CrossRef
30.
Stephens NS, Siffledeen J, Su X, Murdoch TB, Fedorak RN, Slupsky CM. Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J Crohns Colitis. 2013;7:e42–8.CrossRef
31.
Menni C, Fauman E, Erte I, Perry JRB, Kastenmüller G, Shin S-Y, et al. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes. 2013;62:4270–6.CrossRef
32.
Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016;165:780–91.CrossRef
33.
Bruggeman FJ, Westerhoff HV. The nature of systems biology. Trends Microbiol. 2007;15:45–50.CrossRef
34.
Davis MM, Tato CM, Furman D. Systems immunology: just getting started. Nat Immunol. 2017;18:725–32.CrossRef
35.
Germain RN. Will systems biology deliver its promise and contribute to the development of new or improved vaccines? What really constitutes the study of “systems biology” and how might such an approach facilitate vaccine design. Cold Spring Harb Perspect Biol. 2017. https://​doi.​org/​10.​1101/​cshperspect.​a033308
36.
Lever M, Lim H-S, Kruger P, Nguyen J, Trendel N, Abu-Shah E, et al. Architecture of a minimal signaling pathway explains the T-cell response to a 1 million-fold variation in antigen affinity and dose. Proc Natl Acad Sci U S A. 2016;113:E6630–8.CrossRef
37.
François P, Voisinne G, Siggia ED, Altan-Bonnet G, Vergassola M. Phenotypic model for early T-cell activation displaying sensitivity, specificity, and antagonism. Proc Natl Acad Sci U S A. 2013;110:E888–97.CrossRef
38.
Mukhopadhyay H, de Wet B, Clemens L, Maini PK, Allard J, van der Merwe PA, et al. Multisite phosphorylation modulates the T cell receptor ζ-chain potency but not the Switchlike response. Biophys J. 2016;110:1896–906.CrossRef
39.
Chevrier N, Mertins P, Artyomov MN, Shalek AK, Iannacone M, Ciaccio MF, et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell. 2011;147:853–67.CrossRef
40.
Yosef N, Shalek AK, Gaublomme JT, Jin H, Lee Y, Awasthi A, et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature. 2013;496:461–8.CrossRef
41.
Kowalczyk MS, Tirosh I, Heckl D, Rao TN, Dixit A, Haas BJ, et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 2015;25:1860–72.CrossRef
42.
Shay T, Jojic V, Zuk O, Rothamel K, Puyraimond-Zemmour D, Feng T, et al. Conservation and divergence in the transcriptional programs of the human and mouse immune systems. Proc Natl Acad Sci U S A. 2013;110:2946–51.CrossRef
43.
Brandes M, Klauschen F, Kuchen S, Germain RN. A systems analysis identifies a feedforward inflammatory circuit leading to lethal influenza infection. Cell. 2013;154:197–212.CrossRef
44.
Nakaya HI, Pulendran B. Vaccinology in the era of high-throughput biology. Philos Trans R Soc Lond Ser B Biol Sci. 2015;370:20140146.CrossRef
45.
Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10:116–25.CrossRef
46.
Ravindran R, Khan N, Nakaya HI, Li S, Loebbermann J, Maddur MS, et al. Vaccine activation of the nutrient sensor GCN2 in dendritic cells enhances antigen presentation. Science. 2014;343:313–7.CrossRef
47.
Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, et al. Systems biology of vaccination for seasonal influenza in humans. Nat Immunol. 2011;12:786–95.CrossRef
48.
Bucasas KL, Franco LM, Shaw CA, Bray MS, Wells JM, Niño D, et al. Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J Infect Dis. 2011;203:921–9.CrossRef
49.
Zak DE, Andersen-Nissen E, Peterson ER, Sato A, Hamilton MK, Borgerding J, et al. Merck Ad5/HIV induces broad innate immune activation that predicts CD8+ T-cell responses but is attenuated by preexisting Ad5 immunity. Proc Natl Acad Sci U S A. 2012;109:E3503–12.CrossRef
50.
Vahey MT, Wang Z, Kester KE, Cummings J, Heppner DG, Nau ME, et al. Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine. J Infect Dis. 2010;201:580–9.CrossRef
51.
Reif DM, Motsinger-Reif AA, McKinney BA, Rock MT, Crowe JE, Moore JH. Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination. Genes Immun. 2009;10:112–9.CrossRef
52.
Gunawardena J. Beware the tail that wags the dog: informal and formal models in biology. Mol Biol Cell. 2014;25:3441–4.CrossRef
53.
Motta S, Pappalardo F. Mathematical modeling of biological systems. Brief Bioinf. 2013;14:411–22.CrossRef
54.
Le Novère N. Quantitative and logic modelling of molecular and gene networks. Nat Rev Genet. 2015;16:146–58.CrossRef
55.
Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44:1–12.CrossRef
56.
Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA. Logic-based models for the analysis of cell signaling networks. Biochemistry. 2010;49:3216–24.CrossRef
57.
Hoffmann A, Levchenko A, Scott ML, Baltimore D. The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation. Science. 2002;298:1241–5.CrossRef
58.
Altan-Bonnet G, Germain RN. Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 2005;3:e356.CrossRef
59.
Bains I, Thiébaut R, Yates AJ, Callard R. Quantifying thymic export: combining models of naive T cell proliferation and TCR excision circle dynamics gives an explicit measure of thymic output. J Immunol. 2009;183:4329–36.CrossRef
60.
Mayer H, Zaenker KS, An Der Heiden U. A basic mathematical model of the immune response. Chaos. 1995;5:155–61.CrossRef
61.
Polak ME, Ung CY, Masapust J, Freeman TC, Ardern-Jones MR. Petri Net computational modelling of Langerhans cell interferon regulatory factor network predicts their role in T cell activation. Sci Rep. 2017;7:668.CrossRef
62.
Mendoza L. A network model for the control of the differentiation process in Th cells. Biosystems. 2006;84:101–14.CrossRef
63.
Zhang R, Shah MV, Yang J, Nyland SB, Liu X, Yun JK, et al. Network model of survival signaling in large granular lymphocyte leukemia. Proc Natl Acad Sci U S A. 2008;105:16308–13.CrossRef
64.
Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris MK, et al. CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol. 2012;6:133.CrossRef
65.
Batt G, Besson B, Ciron P-E, de Jong H, Dumas E, Geiselmann J, et al. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. Methods Mol Biol. 2012;804:439–62.CrossRef
66.
Müssel C, Hopfensitz M, Kestler HA. BoolNet – an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics. 2010;26:1378–80.CrossRef
67.
Benoist C, Germain RN, Mathis D. A plaidoyer for “systems immunology”. Immunol Rev. 2006;210:229–34.CrossRef
68.
Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol. 2007;3:78.CrossRef
69.
Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5:101–13.CrossRef
70.
Carter SL, Brechbühler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20:2242–50.CrossRef
71.
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:17.MathSciNetCrossRef
72.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50.CrossRef
73.
Amar D, Safer H, Shamir R. Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput Biol. 2013;9:e1002955.CrossRef
74.
Watson M. CoXpress: differential co-expression in gene expression data. BMC Bioinform. 2006;7:509.CrossRef
75.
Ha MJ, Baladandayuthapani V, Do K-A. DINGO: differential network analysis in genomics. Bioinformatics. 2015;31:3413–20.CrossRef
76.
Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39:D691–7.CrossRef
77.
Dolinski K, Troyanskaya OG. Implications of Big Data for cell biology. Mol Biol Cell. 2015;26:2575–8.CrossRef
78.
Li B, Tsoi LC, Swindell WR, Gudjonsson JE, Tejasvi T, Johnston A, et al. Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol. 2014;134:1828–38.CrossRef
79.
Gardinassi LG, Garcia GR, Costa CHN, Costa Silva V, de Miranda Santos IKF. Blood transcriptional profiling reveals immunological signatures of distinct states of infection of humans with Leishmania infantum. PLoS Negl Trop Dis. 2016;10:e0005123.CrossRef
80.
Pandey G, Cohain A, Miller J, Merad M. Decoding dendritic cell function through module and network analysis. J Immunol Methods. 2013;387:71–80.CrossRef
81.
Berry MPR, Graham CM, McNab FW, Xu Z, Bloch SAA, Oni T, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466:973–7.CrossRef
82.
Mejias A, Dimo B, Suarez NM, Garcia C, Suarez-Arrabal MC, Jartti T, et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS Med. 2013;10:e1001549.CrossRef
83.
Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 2013;8:2502–15.CrossRef
84.
Jee J, Rozowsky J, Yip KY, Lochovsky L, Bjornson R, Zhong G, et al. ACT: aggregation and correlation toolbox for analyses of genome tracks. Bioinformatics. 2011;27:1152–4.CrossRef
85.
Hunter PJ, Crampin EJ, Nielsen PMF. Bioinformatics, multiscale modeling and the IUPS Physiome project. Brief Bioinf. 2008;9:333–43.CrossRef
Metadata
Title
Systems Immunology
Authors
Melissa Lever
Thiago C. Hirata
Pedro S. T. Russo
Helder I. Nakaya
Copyright Year
2018
Book
Theoretical and Applied Aspects of Systems Biology

Print ISBN: 978-3-319-74973-0

Electronic ISBN: 978-3-319-74974-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-74974-7_9

Premium Partner

    Image Credits