Abstract
While the ever-increasing amounts of scientific data provide a more detailed description of toxic effects, it is not trivial to extract information that will contribute to a better biological understanding. Sophisticated computational methods have been developed to separate mathematically the biological signal from the noise in high-throughput datasets; however, visualizing and putting a signal into a relevant biological context using a priori knowledge is equally important. The Causal Biological Network (CBN) Database addresses these pressing needs in pulmonary and vascular biology. Each of the network models deposited in the CBN Database is scripted in the Biological Expression Language (BEL), a semantic language that represents scientific findings in a computable format. The biological areas covered by the CBN models include cell fate, response to cell stress, cell proliferation, inflammation, tissue repair, and angiogenesis, all in the context of the pulmonary and vascular systems. With specific biological boundaries, multiple types of gene-centric entities, literature support at edge level, and interactive visualization, the CBN Database offers a coherent illustration of important biological processes. Moreover, the computability of the CBN models provides the possibility of data-driven enhancement that delivers an efficient combination of literature knowledge and high-throughput data in a single model. The CBN Database can be applied in computational toxicology and could be extended to drug discovery, biomarker identification, and personalized medicine.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dennis G Jr, Sherman BT, Hosack DA et al (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4:P3
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 102:15545–15550
Joshi-Tope G, Gillespie M, Vastrik I et al (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–D432
Nishimura D (2001) BioCarta. Biotech software & internet report. Comput Software J Sci 2:117–120
Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:27–30
Croft D, Mundo AF, Haw R et al (2014) The reactome pathway knowledgebase. Nucleic Acids Res 42:D472–D477
Kanehisa M, Goto S, Sato Y et al (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:D199–D205
Elkon R, Vesterman R, Amit N et al (2008) SPIKE–a database, visualization and analysis tool of cellular signaling pathways. BMC Bioinformatics 9:110
Paz A, Brownstein Z, Ber Y et al (2010) SPIKE: a database of highly curated human signaling pathways. Nucleic Acids Res 39(Database issue):793–799
Li J, Ning Y, Hedley W et al (2002) The molecule pages database. Nature 420:716–717
Saunders B, Lyon S, Day M et al (2008) The molecule pages database. Nucleic Acids Res 36:D700–D706
Schaefer CF, Anthony K, Krupa S et al (2009) PID: the pathway interaction database. Nucleic Acids Res 37:D674–D679
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 14:340
Hoeng J, Deehan R, Pratt D et al (2012) A network-based approach to quantifying the impact of biologically active substances. Drug Discov Today 17:413–418
Hoeng J, Talikka M, Martin F et al (2013) Case study: the role of mechanistic network models in systems toxicology. Drug Discov Today 19:183–192
Martin F, Thomson TM, Sewer A et al (2012) Assessment of network perturbation amplitude by applying high-throughput data to causal biological networks. BMC Syst Biol 6:54
Thomson TM, Sewer A, Martin F et al (2013) Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol Appl Pharmacol 272:863–878
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 15:238
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
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 5:105
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 5:168
Fujii-Kuriyama Y, Mimura J (2005) Molecular mechanisms of AhR functions in the regulation of cytochrome P450 genes. Biochem Biophys Res Commun 338:311–317
Sagredo C, Øvrebø S, Haugen A et al (2006) Quantitative analysis of benzo-a-pyrene biotransformation and adduct formation in Ahr knockout mice. Toxicol Lett 167:173–182
Baulig A, Garlatti M, Bonvallot V et al (2003) Involvement of reactive oxygen species in the metabolic pathways triggered by diesel exhaust particles in human airway epithelial cells. Am J Physiol Lung Cell Mol Physiol 285:L671–L679
Ferecatu I, Borot M-C, Bossard C et al (2010) Polycyclic aromatic hydrocarbon components contribute to the mitochondria-antiapoptotic effect of fine particulate matter on human bronchial epithelial cells via the aryl hydrocarbon receptor. Part Fibre Toxicol 7:18–32
Rouse RL, Murphy G, Boudreaux MJ et al (2008) Soot nanoparticles promote biotransformation, oxidative stress, and inflammation in murine lungs. Am J Respir Cell Mol Biol 39:198–207
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, doi:10.1155/2013/512086
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 7:167–192
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 7:97–117
Park J, Schlage W, Frushour B et al (2013) Construction of a computable network model of tissue repair and angiogenesis in the lung. J Clin Toxicol S12:2161-0495
De Leon H, Boue S, Schlage WK et al (2014) A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability. J Transl Med 12:185
Liu T, Baek HA, Yu H et al (2011) FIZZ2/RELM-beta induction and role in pulmonary fibrosis. J Immunol 187:450–461
Costello CM, Howell K, Cahill E et al (2008) Lung-selective gene responses to alveolar hypoxia: potential role for the bone morphogenetic antagonist gremlin in pulmonary hypertension. Am J Physiol Lung Cell Mol Physiol 295:L272–L284
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
Fry DW, Harvey PJ, Keller PR et al (2004) Specific inhibition of cyclin-dependent kinase 4/6 by PD 0332991 and associated antitumor activity in human tumor xenografts. Mol Cancer Ther 3:1427–1438
Fornier M, Rathkopf D, Shah M et al (2007) Phase I dose-finding study of weekly docetaxel followed by flavopiridol for patients with advanced solid tumors. Clin Cancer Res 13:5841–5846
Park WJ, Kothapalli KS, Reardon HT et al (2012) A novel FADS1 isoform potentiates FADS2-mediated production of eicosanoid precursor fatty acids. J Lipid Res 53:1502–1512
Sbv Improver Project Team (2013) On crowd-verification of biological networks. Bioinform Biol Insights 7:307
Ansari S, Binder J, Boue S, Di Fabio A, Hayes W, sbv Ipt et al (2013) On crowd-verification of biological networks. Bioinform Biol Insights 7:307–25, Pubmed Central PMCID: 3798292. Epub 2013/10/24. eng
sbv Improver Network Verification Challenge. https://bionet.sbvimprover.com
sbv Improver Project Team, Boue S, FieldsB, Hoeng J, Park J, Peitsch MC, Schlage WK, et al. (2015) Enhancement of COPD biological networks using a web-based collaboration interface.F1000Res 4
Boue S, Talikka M, Westra JW, Hayes W, Di Fabio A, Park J et al (2015) Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database 2015, PubMed
Szostak, J, Ansari, S, Madan S et al. (2015) Construction of biological networks from unstructured information based on a semiautomated curation workflow. Database. In press
Fielden MR, Brennan R, Gollub J (2007) A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals. Toxicol Sci 99:90–100
Huang J, Shi W, Zhang J et al (2010) Genomic indicators in the blood predict drug-induced liver injury. Pharmacogenomics J 10:267–277
Toedter G, Li K, Sague S et al (2012) Genes associated with intestinal permeability in ulcerative colitis: changes in expression following infliximab therapy. Inflamm Bowel Dis 18:1399–1410
Andersen ME, Clewell HJ 3rd, Bermudez E et al (2010) Formaldehyde: integrating dosimetry, cytotoxicity, and genomics to understand dose-dependent transitions for an endogenous compound. Toxicol Sci 118:716–731
Monticello TM, Swenberg JA, Gross EA et al (1996) Correlation of regional and nonlinear formaldehyde-induced nasal cancer with proliferating populations of cells. Cancer Res 56:1012–1022
Hamadeh HK, Bushel PR, Jayadev S et al (2002) Gene expression analysis reveals chemical-specific profiles. Toxicol Sci 67:219–231
Waring JF, Jolly RA, Ciurlionis R et al (2001) Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Toxicol Appl Pharmacol 175:28–42
Acknowledgements
The research described in this chapter of the book was funded by Philip Morris International.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this protocol
Cite this protocol
Talikka, M., Boue, S., Schlage, W.K. (2015). Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems. 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_3
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2778-4_3
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