Skip to main content

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

  • Protocol
  • First Online:
Computational Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1819))

Abstract

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Thakar J, Christensen C, Albert R (2008) Toward understanding the structure and function of cellular interaction networks. Bolyai Soc Math Stud 18:239–275

    Article  Google Scholar 

  2. Anafi RC, Francey LJ, Hogenesch JB et al (2017) CYCLOPS reveals human transcriptional rhythms in health and disease. Proc Natl Acad Sci 114:201619320

    Article  Google Scholar 

  3. Thakar J, Pilione M, Kirimanjeswara G et al (2007) Modeling systems-level regulation of host immune responses. PLoS Comput Biol 3:1022–1039

    Article  CAS  Google Scholar 

  4. Thakar J, Christensen C, Albert R (2008) Toward understanding the structure and function of cellular interaction networks. Bolyai Soc Math Stud 18:239–275

    Article  Google Scholar 

  5. Prescott TP, Papachristodoulou A (2014) Layered decomposition for the model order reduction of timescale separated biochemical reaction networks. J Theor Biol 356:113–122

    Article  CAS  Google Scholar 

  6. Berenstein AJ, Magariños MP, Chernomoretz A et al (2016) A multilayer network approach for guiding drug repositioning in neglected diseases. PLoS Negl Trop Dis 10:e0004300

    Article  Google Scholar 

  7. Kanehisa M, Furumichi M, Tanabe M et al (2016) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:353–361

    Article  Google Scholar 

  8. Wrzodek C, Büchel F, Ruff M et al (2013) Precise generation of systems biology models from KEGG pathways. BMC Syst Biol 7:15

    Article  Google Scholar 

  9. Christensen C, Thakar J, Albert R (2007) Systems-level insights into cellular regulation: inferring, analysing, and modelling intracellular networks. IET Syst Biol 1:61–67

    Article  CAS  Google Scholar 

  10. Shen-orr SS, Goldberger O, Garten Y et al (2009) Towards a cytokine-cell interaction knowledgebase of the adaptive immune system. Pac Symp Biocomput 2009:439–450

    Google Scholar 

  11. Thakar J, Hartmann BM, Marjanovic N et al (2015) Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism. BMC Immunol 16:46

    Article  Google Scholar 

  12. Hartmann BM, Thakar J, Albrecht RA et al (2015) Human dendritic cell response signatures distinguish 1918, pandemic, and seasonal H1N1 influenza viruses. J Virol 89:10190–10205

    Article  CAS  Google Scholar 

  13. Bjornson ZB, Nolan GP, Fantl WJ (2013) Single-cell mass cytometry for analysis of immune system functional states. Curr Opin Immunol 25:484–494

    Article  CAS  Google Scholar 

  14. Brodin P, Jojic V, Gao T et al (2015) Variation in the human immune system is largely driven by non-heritable influences. Cell 160:37–47

    Article  CAS  Google Scholar 

  15. Campbell C, Thakar J, Albert RR (2011) Network analysis reveals cross-links of the immune pathways activated by bacteria and allergen. Phys Rev E Stat Nonlinear Soft Matter Phys 84:1–12

    Article  Google Scholar 

  16. Liu H, Zhang F, Mishra SK et al (2016) Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data. Sci Rep 6:35652

    Article  CAS  Google Scholar 

  17. Katanic D, Khan A, Thakar J (2016) PathCellNet: cell-type specific pathogen-response network explorer. J Immunol Methods 439:15–22

    Article  CAS  Google Scholar 

  18. Hornbeck PV, Zhang B, Murray B et al (2015) PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43:D512–D520

    Article  CAS  Google Scholar 

  19. Prasad TSK, Goel R, Kandasamy K et al (2009) Human protein reference database — 2009 update. Nucleic Acids Res 37:767–772

    Article  Google Scholar 

  20. Salwinski L, Miller CS, Smith AJ et al (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32:449–451

    Article  Google Scholar 

  21. Jewison T, Su Y, Disfany FM et al (2014) SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res 42:478–484

    Article  Google Scholar 

  22. Kandasamy K, Mohan SS, Raju R et al (2010) NetPath: a public resource of curated signal transduction pathways. Genome Biol 11:R3

    Article  Google Scholar 

  23. Kutmon M, Riutta A, Nunes N et al (2016) WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res 44:D488–D494

    Article  CAS  Google Scholar 

  24. Joshi-Tope G, Gillespie M, Vastrik I et al (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33

    Google Scholar 

  25. Croft D, Mundo A, Haw R et al (2014) The reactome pathway knowledgebase. Nucleic acids 42:D472–D477

    Article  CAS  Google Scholar 

  26. Fabregat A, Sidiropoulos K, Garapati P et al (2016) The reactome pathway knowledgebase. Nucleic Acids Res 44:D481–D487

    Article  CAS  Google Scholar 

  27. Romero P, Wagg J, Green ML et al (2005) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6:R2

    Article  Google Scholar 

  28. Chatr-aryamontri A, Oughtred R, Boucher L et al (2017) The BioGRID interaction database: 2017 update. Nucleic Acids Res 45:369–379

    Article  Google Scholar 

  29. Stark C (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539

    Article  CAS  Google Scholar 

  30. Cerami EG, Gross BE, Demir E et al (2011) Pathway commons, a web resource for biological pathway data. Nucleic Acids Res 39:685–690

    Article  Google Scholar 

  31. Keating SM, Le Novère N (2013) Supporting SBML as a model exchange format in software applications. Methods Mol Biol 1021:201–225

    Article  Google Scholar 

  32. Demir E, Cary MP, Paley S et al (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28:935–942

    Article  CAS  Google Scholar 

  33. Habermann B, Villaveces J, Koti P (2015) Tools for visualization and analysis of molecular networks, pathways, and -omics data. Adv Appl Bioinforma Chem 8:11

    Google Scholar 

  34. Jensen LJ, Saric J, Bork P (2006) Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet 7:119–129

    Article  CAS  Google Scholar 

  35. Van Landeghem S, De Bodt S, Drebert ZJ et al (2013) The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis. Plant Cell 25:794–807

    Article  Google Scholar 

  36. Snel B, Lehmann G, Bork P et al (2000) STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res 28:3442–3444

    Article  CAS  Google Scholar 

  37. Hur J, Ozgür A, Xiang Z et al (2012) Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining. J Biomed Semant 3:18

    Article  Google Scholar 

  38. Studham ME, Tjärnberg A, Nordling TEM et al (2014) Functional association networks as priors for gene regulatory network inference. Bioinformatics 30:130–138

    Article  Google Scholar 

  39. Szklarczyk D, Morris JH, Cook H et al (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368

    Article  CAS  Google Scholar 

  40. Margolin AA, Nemenman I, Basso K et al (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7:S7

    Article  Google Scholar 

  41. Marbach D, Prill RJ, Schaffter T et al (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A 107:6286–6291

    Article  CAS  Google Scholar 

  42. Song L, Langfelder P, Horvath S (2012) Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics. 13:328

    Article  CAS  Google Scholar 

  43. Chaussabel D, Quinn C, Shen J et al (2009) A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29:150–164

    Article  Google Scholar 

  44. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics 9:559

    Article  Google Scholar 

  45. Wang R-S, Albert R (2011) Elementary signaling modes predict the essentiality of signal transduction network components. BMC Syst Biol 5:44

    Article  Google Scholar 

  46. Kachalo S, Zhang R, Sontag E et al (2008) NET-SYNTHESIS: a software for synthesis, inference and simplification of signal transduction networks. Bioinformatics 24:293–295

    Article  CAS  Google Scholar 

  47. Terfve C, Cokelaer T, Henriques D et al (2012) CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol 6:133

    Article  CAS  Google Scholar 

  48. Müssel C, Hopfensitz M, Kestler HA (2010) BoolNet-an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics 26:1378–1380

    Article  Google Scholar 

  49. Oltvai ZN, Barabási A-L, Jeong H et al (2000) The large-scale organization of metabolic networks. Nature 407:651–654

    Article  Google Scholar 

  50. Bollobás B Riordan O (2002) Mathematical results on scale-free random graphs, Handbook of Graphs and Networks: from the Genome to the Internet pp 1–38

    Google Scholar 

  51. Brohée S, van Helden J, Wong L et al (2006) Protein complex prediction based on k -connected subgraphs in protein interaction network. BMC Bioinformatics 7:488

    Article  Google Scholar 

  52. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:Article17

    Article  Google Scholar 

  53. Pavlopoulos G, Wegener A-L, Schneider R (2008) A survey of visualization tools for biological network analysis. BioData Mining 1:12

    Article  Google Scholar 

  54. Castro MA, Wang X, Fletcher MNC et al (2012) RedeR: R/bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations. Genome Biol 13:R29

    Article  CAS  Google Scholar 

  55. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Software-Practice & Experience 21:1129–1164

    Article  Google Scholar 

  56. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  CAS  Google Scholar 

  57. Yamada T, Letunic I, Okuda S et al (2011) IPath2.0: interactive pathway explorer. Nucleic Acids Res 39:412–415

    Article  Google Scholar 

  58. Letunic I, Yamada T, and Kanehisa M et al (2008) iPath: interactive exploration of biochemical pathways and networks

    Google Scholar 

  59. Ellson J, Gansner ER, Koutsofios E et al (2004) Graphviz and Dynagraph -- static and dynamic graph drawing tools. In: Jünger M, Mutzel P (eds) Graph Drawing Software. Springer, Berlin, Heidelberg, pp 127–148

    Chapter  Google Scholar 

  60. Milo R, Shen-Orr S, Itzkovitz S et al (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827

    Article  CAS  Google Scholar 

  61. Shoval O, Alon U (2010) SnapShot: network motifs. Cell 143:326–326.e1

    Article  Google Scholar 

  62. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461

    Article  CAS  Google Scholar 

  63. Shen-Orr SS, Milo R, Mangan S et al (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31:64–68

    Article  CAS  Google Scholar 

  64. Yeger-Lotem E, Sattath S, Kashtan N et al (2004) Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction. Proc Natl Acad Sci U S A 101:5934–5939

    Article  CAS  Google Scholar 

  65. Kashtan N, Itzkovitz S, Milo R et al (2004) Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20:1746–1758

    Article  CAS  Google Scholar 

  66. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Sy 1695:1–9

    Google Scholar 

  67. Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using NetworkX. Proceedings of the 7th Python in Science Conference (SciPy) 2008:11–15

    Google Scholar 

  68. Ibrahim M, Jassim S, Cawthorne M et al (2014) A MATLAB tool for pathway using a topology-based pathway regulation score. BMC Bioinformatics 15:358

    Article  Google Scholar 

  69. Graph (2017) Wolfram Language and System Documentation Center

    Google Scholar 

  70. Thakar J, Poss M, Albert R et al (2010) Dynamic models of immune responses: what is the ideal level of detail? Theor Biol Med Model 7:35

    Article  Google Scholar 

  71. Albert I, Thakar J, Li S et al (2008) Boolean network simulations for life scientists. Source Code Biol Med 3:16

    Article  Google Scholar 

  72. Glass L, Kauffman SA (1973) The logical analysis of continuous, non-linear biochemical control networks. J Theor Biol 39:103–129

    Article  CAS  Google Scholar 

  73. Anderson CS, DeDiego ML, Topham DJ et al (2016) Boolean modeling of cellular and molecular pathways involved in influenza infection. Comput Math Methods Med 2016:1–11

    Article  CAS  Google Scholar 

  74. Saadatpour A, Albert I, Albert R (2010) Attractor analysis of asynchronous Boolean models of signal transduction networks. J Theor Biol 266:641–656

    Article  Google Scholar 

  75. Thakar J, Pathak AK, Murphy L et al (2012) Network model of immune responses reveals key effectors to single and co-infection dynamics by a respiratory bacterium and a gastrointestinal helminth. PLoS Comput Biol 8(1):e1002345

    Article  CAS  Google Scholar 

  76. Walsh ER, Thakar J, Stokes K et al (2011) Computational and experimental analysis reveals a requirement for eosinophil-derived IL-13 for the development of allergic responses in C57BL/6 mice. J Immunol 186:2936–2949

    Article  CAS  Google Scholar 

  77. Thakar J, Saadatpour-Moghaddam A, Harvill ET et al (2009) Constraint-based network model of pathogen-immune system interactions. J R Soc Interface 6:599–612

    Article  CAS  Google Scholar 

  78. Wittmann DM, Krumsiek J, Saez-Rodriguez J et al (2009) Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Syst Biol 3:98

    Article  Google Scholar 

  79. Morris MK, Melas I, Saez-Rodriguez J (2013) Construction of cell type-specific logic models of signaling networks using CellNOpt. Methods Mol Biol 930:179–214

    Article  CAS  Google Scholar 

  80. Morris MK, Saez-Rodriguez J, Sorger PK et al (2010) Logic-based models for the analysis of cell signaling networks. Biochemistry 49:3216–3224

    Article  CAS  Google Scholar 

  81. Aldridge BB, Saez-Rodriguez J, Muhlich JL et al (2009) Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS Comput Biol 5(4):e1000340

    Article  Google Scholar 

  82. Schivo S, Scholma J, van der Vet PE et al (2016) Modelling with ANIMO: between fuzzy logic and differential equations. BMC Syst Biol 10:56

    Article  Google Scholar 

  83. Shmulevich I, Dougherty ER, Kim S et al (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics (Oxford, England) 18:261–274

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juilee Thakar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Palli, R., Thakar, J. (2018). Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases. In: von Stechow, L., Santos Delgado, A. (eds) Computational Cell Biology. Methods in Molecular Biology, vol 1819. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8618-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8618-7_18

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8617-0

  • Online ISBN: 978-1-4939-8618-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics