Skip to main content

Quantifying the Biological Impact of Active Substances Using Causal Network Models

  • Protocol
Computational Systems Toxicology

Abstract

In this chapter a five-step strategy is described that provides comparative evaluations of the effects of biologically active substances. These evaluations constitute an integral part of the determination of the risks for the human population to exposure to these substances. The strategy is based on the concept of biological impact quantification for which novel computational methodologies have been developed in the past few years; these methodologies are reviewed in this chapter. The effects of the active substances are then described in terms of networks containing the biological mechanisms involved in the response to the exposure. As a consequence, the biological impact assessment represents a systems-wide metric of network-based perturbed biological mechanisms. The implementation of the strategy involves the generation of transcriptomics data following the exposure experiment and their evaluation in the context of causal network models. After the five-step strategy for biological impact quantification has been described in some detail, its application in a concrete case of a mouse smoking-cessation experiment is presented. The results show how mechanistic insights into the potential toxic effects of exposure to active substances can benefit the safety assessment.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Similar content being viewed by others

References

  1. U.S. Food and Drug Administration (2004) The critical path initiative. http://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/

    Google Scholar 

  2. Committee on Toxicity Testing and Assessment of Environmental Agents, National Research Council (2007) Toxicity testing in the 21st century: a vision and a strategy. The National Academies Press, Washington, DC, http://www.nap.edu/openbook.php?record_id=11970

    Google Scholar 

  3. Krewski D, Westphal M, Andersen ME et al (2014) A framework for the next generation of risk science. Environ Health Perspect 122(8):796–805. doi:10.1289/ehp.1307260

    PubMed Central  PubMed  Google Scholar 

  4. Waters MD, Fostel JM (2004) Toxicogenomics and systems toxicology: aims and prospects. Nat Rev Genet 5(12):936–948

    Article  CAS  PubMed  Google Scholar 

  5. Hoeng J, Deehan R, Pratt D et al (2012) A network-based approach to quantifying the impact of biologically active substances. Drug Discov Today 17(9-10):413–418, doi:10.1016/j.drudis.2011.11.008 S1359-6446(11)00425-9 [pii]

    Article  PubMed  Google Scholar 

  6. Barabasi A-L, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–113

    Article  CAS  PubMed  Google Scholar 

  7. Kitano H (2002) Systems biology: a brief overview. Science 295(5560):1662–1664

    Article  CAS  PubMed  Google Scholar 

  8. Del Sol A, Balling R, Hood L et al (2010) Diseases as network perturbations. Curr Opin Biotechnol 21(4):566–571

    Article  PubMed  Google Scholar 

  9. Barabási A-L, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68

    Article  PubMed Central  PubMed  Google Scholar 

  10. Berger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25(19):2466–2472

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Chindelevitch L, Ziemek D, Enayetallah A et al (2012) Causal reasoning on biological networks: interpreting transcriptional changes. Bioinformatics 28(8):1114–1121. doi:10.1093/bioinformatics/bts090

    Article  CAS  PubMed  Google Scholar 

  12. Kramer A, Green J, Pollard J Jr et al (2014) Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30(4):523–530. doi:10.1093/bioinformatics/btt703

    Article  PubMed Central  PubMed  Google Scholar 

  13. Catlett NL, Bargnesi AJ, Ungerer S et al (2013) Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data. BMC Bioinformatics 14(1):340

    Article  PubMed Central  PubMed  Google Scholar 

  14. Sewer A, Hoeng J, Deehan R et al. Systems biology approaches for compound testing. Data Min Drug Discov 291–316

    Google Scholar 

  15. Hoeng J, Talikka M, Martin F et al (2014) Case study: the role of mechanistic network models in systems toxicology. Drug Discov Today 19(2):183–192. doi:10.1016/j.drudis.2013.07.023

    Article  CAS  PubMed  Google Scholar 

  16. Miller GW (2014) Improving reproducibility in toxicology. Toxicol Sci 139(1):001–003

    Article  CAS  Google Scholar 

  17. Selventa The openBEL portal. http://www.openbel.org/

  18. Martin F, Thomson TM, Sewer A et al (2012) Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks. BMC Syst Biol 6:54. doi:10.1186/1752-0509-6-54

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Mitrea C, Taghavi Z, Bokanizad B et al (2013) Methods and approaches in the topology-based analysis of biological pathways. Frontiers Physiol 4

    Google Scholar 

  20. Martin F, Sewer A, Talikka M et al (2014) Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models. BMC Bioinformatics 15(1):238

    Article  PubMed Central  PubMed  Google Scholar 

  21. The Core R team (2012) R: a language and environment for statistical computing

    Google Scholar 

  22. Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80

    Article  PubMed Central  PubMed  Google Scholar 

  23. Gentleman R, Lang DT (2007) Statistical analyses and reproducible research. J Comput Graph Stat 16(1)

    Google Scholar 

  24. Xie Y (2014) Knitr: a comprehensive tool for reproducible research in R. Implement Reprod Res 1:20

    Google Scholar 

  25. Liu Z, Pounds S (2014) An R package that automatically collects and archives details for reproducible computing. BMC Bioinformatics 15(1):138

    Article  PubMed Central  PubMed  Google Scholar 

  26. Rhrissorrakrai K, Belcastro V, Bilal E et al (2014) Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge. Bioinformatics 31(4):471–483, 10.1093/bioinformatics/btu611

    Article  PubMed Central  PubMed  Google Scholar 

  27. Wikipedia. The three Rs (animals). http://en.wikipedia.org/wiki/The_Three_Rs_(animals)

    Google Scholar 

  28. Commission TE (2009) Alternative testing strategies – progress report 2009 – replacing, reducing and refining use of animals in research. Office for Official Publications of the European Communities, Luxembourg

    Google Scholar 

  29. Edwards SW, Preston RJ (2008) Systems biology and mode of action based risk assessment. Toxicol Sci 106(2):312–318. doi:10.1093/toxsci/kfn190

    Article  CAS  PubMed  Google Scholar 

  30. Russell WMS, Burch RL, Hume CW (1959) The principles of humane experimental technique

    Google Scholar 

  31. Pleil JD, Sheldon LS (2011) Adapting concepts from systems biology to develop systems exposure event networks for exposure science research. Biomarkers 16(2):99–105

    Article  CAS  PubMed  Google Scholar 

  32. Rustici G, Kolesnikov N, Brandizi M et al (2013) ArrayExpress update—trends in database growth and links to data analysis tools. Nucleic Acids Res 41(D1):D987–D990

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Barrett T, Wilhite SE, Ledoux P et al (2013) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41(D1):D991–D995

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Allison DB, Cui X, Page GP et al (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7(1):55–65

    Article  CAS  PubMed  Google Scholar 

  35. Shi L, Shi L, Reid LH et al (2006) The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24(9):1151–1161

    Article  CAS  PubMed  Google Scholar 

  36. Yang YH, Speed T (2002) Design issues for cDNA microarray experiments. Nat Rev Genet 3(8):579–588

    CAS  PubMed  Google Scholar 

  37. Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays. Nat Genet 32:490–495

    Article  CAS  PubMed  Google Scholar 

  38. Lockhart DJ, Winzeler EA (2000) Genomics, gene expression and DNA arrays. Nature 405(6788):827–836

    Article  CAS  PubMed  Google Scholar 

  39. Ozsolak F, Milos PM (2010) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12(2):87–98

    Article  PubMed Central  PubMed  Google Scholar 

  40. Nuwaysir EF, Bittner M, Trent J et al (1999) Microarrays and toxicology: the advent of toxicogenomics. Mol Carcinog 24(3):153–159

    Article  CAS  PubMed  Google Scholar 

  41. Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299

    Article  CAS  PubMed  Google Scholar 

  42. Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264

    Article  PubMed  Google Scholar 

  43. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304

    Article  PubMed  Google Scholar 

  44. Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3(1)

    Google Scholar 

  45. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57(1):289–300

    Google Scholar 

  46. Suárez E, Burguete A, Mclachlan GJ (2009) Microarray data analysis for differential expression: a tutorial. P R Health Sci J 28(2)

    Google Scholar 

  47. Gershon D (2005) DNA microarrays: more than gene expression. Nature 437(7062):1195–1198

    Article  CAS  PubMed  Google Scholar 

  48. Hermida L, Poussin C, Stadler MB et al (2013) Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data. BMC Genomics 14(1):514

    Article  PubMed Central  PubMed  Google Scholar 

  49. Surowiecki J (2005) The wisdom of crowds. Random House LLC, London

    Google Scholar 

  50. Ansari S, Binder J, Boue S et al (2013) On crowd-verification of biological networks. Bioinform Biol Insights 7:307

    PubMed Central  PubMed  Google Scholar 

  51. Westra JW, Schlage WK, Frushour BP et al (2011) Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC Syst Biol 5(1):105

    Article  PubMed Central  PubMed  Google Scholar 

  52. Schlage WK, Westra JW, Gebel S et al (2011) A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC Syst Biol 5(1):168

    Article  PubMed Central  PubMed  Google Scholar 

  53. Gebel S, Lichtner RB, Frushour B et al (2013) Construction of a computable network model for DNA damage, autophagy, cell death, and senescence. Bioinform Biol Insights 7:97

    PubMed Central  PubMed  Google Scholar 

  54. Westra JW, Schlage WK, Hengstermann A et al (2013) A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue. Bioinform Biol Insights 7:167

    PubMed Central  PubMed  Google Scholar 

  55. De León H, Boué S, Schlage WK et al (2014) A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability. J Transl Med 12(1):185

    Article  PubMed Central  PubMed  Google Scholar 

  56. Boué S, Talikka M, Westra JW et al (2014) Causal Biological Network (CBN) database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Submitted

    Google Scholar 

  57. The SBVImprover project team. The SBVImprover Bionet platform. http://bionet.sbvimprover.com

    Google Scholar 

  58. Belcastro V, Poussin C, Gebel S et al (2013) Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset. Bioinform Biol Insights 7:217

    Article  PubMed Central  PubMed  Google Scholar 

  59. Liao JC, Boscolo R, Yang Y-L et al (2003) Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci 100(26):15522–15527

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  60. Thomson TM, Sewer A, Martin F et al (2013) Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol Appl Pharmacol 272(3):863–878. doi:10.1016/j.taap.2013.07.007

    Article  CAS  PubMed  Google Scholar 

  61. Tarca AL, Lauria M, Unger M et al (2013) Strengths and limitations of microarray-based phenotype prediction: lessons learned from the improver diagnostic signature challenge. Bioinformatics 29(22):2892–2899

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  62. Kiyosawa N, Manabe S, Yamoto T et al (2010) Practical application of toxicogenomics for profiling toxicant-induced biological perturbations. Int J Mol Sci 11(9):3397–3412. doi:10.3390/ijms11093397

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  63. Vasilyev DM, Thomson TM, Frushour BP et al (2014) An algorithm for score aggregation over causal biological networks based on random walk sampling. BMC Res Notes 7(1):516

    Article  PubMed Central  PubMed  Google Scholar 

  64. Gonzalez-Suarez I, Sewer A, Walker P et al (2014) Systems biology approach for evaluating the biological impact of environmental toxicants in vitro. Chem Res Toxicol. doi:10.1021/tx400405s

    PubMed  Google Scholar 

  65. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8(2), e1002375. doi:10.1371/journal.pcbi.1002375

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  66. Phillips B, Veljkovic E, Peck MJ et al (2014) A 7-month cigarette smoke inhalation study in C57BL/6 mice demonstrates reduced lung inflammation and emphysema following smoking cessation or aerosol exposure from a prototypic modified risk tobacco product

    Google Scholar 

  67. Canada H (1999) Determination of "Tar" and nicotine in sidestream tobacco smoke. Health Canada Test Method T-115

    Google Scholar 

  68. Kogel U, Schlage WK, Martin F et al (2014) A 28-day rat inhalation study with an integrated molecular toxicology endpoint demonstrates reduced exposure effects for a prototypic modified risk tobacco product compared with conventional cigarettes. Food Chem Toxicol 68:204–217

    Article  CAS  PubMed  Google Scholar 

  69. Iskandar AR, Martin F, Talikka M et al (2013) Systems approaches evaluating the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues. BioMed Res Int 2013:512086

    Article  PubMed Central  PubMed  Google Scholar 

  70. Meyer P, Hoeng J, Rice JJ et al (2012) Industrial methodology for process verification in research (IMPROVER): toward systems biology verification. Bioinformatics 28(9):1193–1201

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Mathis C, Poussin C, Weisensee D et al (2013) Human bronchial epithelial cells exposed in vitro to cigarette smoke at the air-liquid interface resemble bronchial epithelium from human smokers. Am J Physiol Lung Cell Mol Physiol 304(7):L489–L503

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  72. Poussin C, Gallitz I, Schlage WK et al (2014) Mechanism of an indirect effect of aqueous cigarette smoke extract on the adhesion of monocytic cells to endothelial cells in an in vitro assay revealed by transcriptomics analysis. Toxicol In Vitro 28(5):896–908

    Article  CAS  PubMed  Google Scholar 

  73. Boué S, De León H, Schlage WK et al (2013) Cigarette smoke induces molecular responses in respiratory tissues of ApoE−/− mice that are progressively deactivated upon cessation. Toxicology 314(1):112–124

    Article  PubMed  Google Scholar 

  74. Xiang Y, Kogel U, Gebel S et al (2014) Discovery of emphysema relevant molecular networks from an A/J mouse inhalation study using reverse engineering and forward simulation (REFS™). Gene Regul Syst Biol 8:45

    Article  Google Scholar 

  75. Luettich K, Xiang Y, Iskandar A et al (2014) Systems toxicology approaches enable mechanistic comparison of spontaneous and cigarette smoke-related lung tumor development in the A/J mouse model. Interdiscip Toxicol 7(2):73–84

    PubMed Central  CAS  PubMed  Google Scholar 

  76. Poussin C, Mathis C, Alexopoulos LG et al (2014) The species translation challenge—a systems biology perspective on human and rat bronchial epithelial cells. Scientific Data 1

    Google Scholar 

  77. Talikka M, Kostadinova R, Xiang Y et al (2014) The response of human nasal and bronchial organotypic tissue cultures to repeated whole cigarette smoke exposure. Int J Toxicol. doi:10.1177/1091581814551647

    PubMed  Google Scholar 

  78. Schlage WK, Iskandar AR, Kostadinova R et al (2014) In vitro systems toxicology approach to investigate the effects of repeated cigarette smoke exposure on human buccal and gingival organotypic epithelial tissue cultures. Toxicol Mech Methods 24(7):470–487

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  79. Castillo-Davis CI, Hartl DL (2003) GeneMerge—post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19(7):891–892

    Article  CAS  PubMed  Google Scholar 

  80. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  81. Efron B, Tibshirani R (2007) On testing the significance of sets of genes. Ann Appl Stat 1(1):107–129

    Article  Google Scholar 

  82. Tomfohr J, Lu J, Kepler TB (2005) Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics 6(1):225

    Article  PubMed Central  PubMed  Google Scholar 

  83. Lee E, Chuang H-Y, Kim J-W et al (2008) Inferring pathway activity toward precise disease classification. PLoS Comput Biol 4(11), e1000217

    Article  PubMed Central  PubMed  Google Scholar 

  84. Tarca AL, Draghici S, Khatri P et al (2009) A novel signaling pathway impact analysis. Bioinformatics 25(1):75–82

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  85. Shojaie A, Michailidis G (2009) Analysis of gene sets based on the underlying regulatory network. J Comput Biol 16(3):407–426

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  86. Komurov K, Dursun S, Erdin S et al (2012) NetWalker: a contextual network analysis tool for functional genomics. BMC Genomics 13(1):282

    Article  PubMed Central  PubMed  Google Scholar 

  87. Rapaport F, Zinovyev A, Dutreix M et al (2007) Classification of microarray data using gene networks. BMC Bioinformatics 8(1):35

    Article  PubMed Central  PubMed  Google Scholar 

  88. Ackermann M, Strimmer K (2009) A general modular framework for gene set enrichment analysis. BMC Bioinformatics 10(1):47

    Article  PubMed Central  PubMed  Google Scholar 

  89. Lefebvre C, Rajbhandari P, Alvarez MJ et al (2010) A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol 6(1)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alain Sewer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Sewer, A., Martin, F., Schlage, W.K., Hoeng, J., Peitsch, M.C. (2015). Quantifying the Biological Impact of Active Substances Using Causal Network Models. In: Hoeng, J., Peitsch, M. (eds) Computational Systems Toxicology. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2778-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2778-4_10

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2777-7

  • Online ISBN: 978-1-4939-2778-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics