Systems toxicology: From basic research to risk assessment

Chemical Research in Toxicology

Volumn 27, Issue 3, 2014, Pages 314-329

  Sturla, Shana J ^a   Boobis, Alan R ^b   Fitzgerald, Rex E ^c   Hoeng, Julia ^d   Kavlock, Robert J ^e   Schirmer, Kristin ^f   Whelan, Maurice ^g   Wilks, Martin F ^c   Peitsch, Manuel C ^d  

^a ETH ZURICH   (Switzerland)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c Swiss Centre for Applied Human Toxicology   (Switzerland)

^d PHILIP MORRIS PRODUCTS S A   (Switzerland)

^e U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^f SWISS FEDERAL INSTITUTE OF AQUATIC SCIENCE AND TECHNOLOGY   (Switzerland)

^g JOINT RESEARCH CENTRE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

XENOBIOTIC AGENT;

ADVERSE OUTCOME; ARTICLE; BASIC RESEARCH; DATA ANALYSIS; DNA ADDUCT; DNA SEQUENCE; ENVIRONMENTAL EXPOSURE; HEALTH HAZARD; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN GENOME PROJECT; INFORMATION SYSTEM; LIPIDOMICS; LONG TERM EXPOSURE; MATHEMATICAL MODEL; METABOLOMICS; NONHUMAN; PROTEIN FUNCTION; PROTEIN PHOSPHORYLATION; PROTEOMICS; PUBLIC HEALTH; QUANTITATIVE ANALYSIS; RISK ASSESSMENT; TOXICOLOGY; TRANSCRIPTOMICS;

ANIMALS; BIOLOGICAL MARKERS; DISEASE MODELS, ANIMAL; DRUG-INDUCED LIVER INJURY; ENVIRONMENTAL EXPOSURE; HUMANS; METABOLOMICS; MODELS, THEORETICAL; PROTEOMICS; RISK ASSESSMENT; XENOBIOTICS;

EID: 84896366331     PISSN: 0893228X     EISSN: 15205010     Source Type: Journal    
DOI: 10.1021/tx400410s     Document Type: Article

References (98)

1. Waters, M. D. and Fostel, J. M. (2004) Toxicogenomics and systems toxicology: Aims and prospects Nat. Rev. Genet. 5, 936-948
2. Hartung, T., van Vliet, E., Jaworska, J., Bonilla, L., Skinner, N., and Thomas, R. (2012) Food for thought... systems toxicology ALTEX 29, 119-128
3. Basketter, D. A., Clewell, H., Kimber, I., Rossi, A., Blaauboer, B., Burrier, R., Daneshian, M., Eskes, C., Goldberg, A., Hasiwa, N., Hoffmann, S., Jaworska, J., Knudsen, T. B., Landsiedel, R., Leist, M., Locke, P., Maxwell, G., McKim, J., McVey, E. A., Ouédraogo, G., Patlewicz, G., Pelkonen, O., Roggen, E., Rovida, C., Ruhdel, I., Schwarz, M., Schepky, A., Schoeters, G., Skinner, N., Trentz, K., Turner, M., Vanparys, P., Yager, J., Zurlo, J., and Hartung, T. (2012) A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing ALTEX 29, 3-91
4. Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes, Official Journal of the European Union, L 276, pp 33-78.
5. Regulation (EC) no 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products, Official Journal of the European Union, L 342, 22.12.2009, pp 59-209.
6. Regulation (EC) no 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the registration, evaluation, authorisation and restriction of chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) no 793/93 and Commission Regulation (EC) no 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC, Official Journal of the European Union, L 136, pp 3-277.
7. Regulation (EC) no 1107/2009 of the European Parliament and of the Council of 21 October 2009 concerning the placing of plant protection products on the market and repealing Council Directives 79/117/EEC and 91/414/EEC, Official Journal of the European Union, L 309, pp 1-50.
8. Regulation (EC) no 528/2012 of the European Parliament and of the Council of 22 May 2012 concerning the making available on the market and use of biocidal products, Official Journal of the European Union, L 167, pp 1-123.
9. Toxicity and assessment of chemical mixtures, Scientific Committee on Health and Environmental Risks (SCHER), Scientific Committee on Emerging and Newly Identified Health Risks (SCENHIR), and Scientific Committee on Consumer Safety (SCCS), joint opinion adopted on 14th December 2011, http://ec.europa.eu/ health/scientific-committees/environmental-risks/opinions/index-en.htm.
10. Bai, J. P. F. and Abernethy, D. R. (2013) Systems pharmacology to predict drug toxicity: Integration across levels of biological organization Annu. Rev. Pharmacol. Toxicol. 53, 451-473
11. Wambaugh, J. F., Setzer, R. W., Reif, D. M., Gangwal, S., Mitchell-Blackwood, J., Arnot, J. A., Joliet, O., Frame, A., Rabinowitz, J., Knudsen, T. B., Judson, R. S., Egeghy, P., Vallero, D., and Cohen Hubal, E. A. (2013) High-throughput models for exposure-based chemical prioritization in the ExpoCast project Environ. Sci. Technol. 47, 8479-8488
12. Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., Mount, D. R., Nichols, J. W., Russom, C. L., Schmieder, P. K., Serrrano, J. A., Tietge, J. E., and Villeneuve, D. L. (2010) Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment Environ. Toxicol. Chem. 29, 730-741
13. Guidance document on developing and assessing adverse outcome pathways, series on testing and assessment, Organisation for Economic Co-operation and Development (OECD), no. 184n ENV/JM/MONO(2013)6.
14. Systems Toxicology 2013-From Basic Research to Human Risk Assessment, Ascona, Switzerland, April 28-May 1, 2013. http://www.systox2013.ch/.
15. Ezzati, M., Hoorn, S. V., Lopez, A. D., Danaei, G., Rodgers, A., Mathers, C. D., and Murray, C. J. L. (2006) Comparative quantification of mortality and burden of disease attributable to selected risk factors, in Global Burden of Disease and Risk Factors (Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D. T., and Murray, C. J. L., Eds.) Chapter 4, World Bank, Washington, DC.
16. Rappaport, S. M. (2012) Discovering environmental causes of disease J. Epidemiol. Community Health 66, 99-102
17. Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., Cho, J. H., Guttmacher, A. E., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C. N., Slatkin, M., Valle, D., Whittemore, A. S., Boehnke, M., Clark, A. G., Eichler, E. E., Gibson, G., Haines, J. L., Mackay, T. F., McCarroll, S. A., and Visscher, P. M. (2009) Finding the missing heritability of complex diseases Nature 461, 747-753
18. Wild, C. P. (2005) Complementing the genome with an "exposome": The outstanding challenge of environmental exposure measurement in molecular epidemiology Cancer Epidemiol., Biomarkers Prev. 14, 1847-1850
19. Wild, C. P. (2012) The exposome: From concept to utility Int. J. Epidemiol. 41, 24-32
20. Escher, B. I. and Hermens, J. L. M. (2004) Internal exposure: Linking bioavailability to effects Environ. Sci. Technol. 38, 455A-462A
21. Patel, C. J., Cullen, M. R., Ioannidis, J. P., and Butte, A. J. (2012) Systematic evaluation of environmental factors: Persistent pollutants and nutrients correlated with serum lipid levels Int. J. Epidemiol. 41, 828-843
22. Lind, P. M., Risérus, U., Salihovic, S., Bavel, Bv., and Lind, L. (2013) An environmental wide association study (EWAS) approach to the metabolic syndrome Environ. Int. 55, 1-8
23. Rappaport, S. M. and Smith, M. T. (2010) Epidemiology. Environment and disease risks Science 330, 460-461
24. Rappaport, S. M. (2012) Biomarkers intersect with the exposome Biomarkers 17, 483-489
25. (1993) WHO International Programme on Chemical Safety, Environmental Health Criteria 155, Biomarkers and risk assessment: Concepts and principles, World Health Organization, Geneva, Switzerland.
26. Ritchie, S. A., Tonita, J., Alvi, R., Lehotay, D., Elshoni, H., Myat, S., McHattie, J., and Goodenowe, D. B. (2013) Low-serum GTA-446 anti-inflammatory fatty acid levels as a new risk factor for colon cancer Int. J. Cancer 132, 355-362
27. Ho, S. M., Johnson, A., Tarapore, P., Janakiram, V., Zhang, X., and Leung, Y. K. (2012) Environmental epigenetics and its implication on disease risk and health outcomes ILAR J. 53, 289-305
28. Talikka, M., Sierro, N., Ivanov, N. V., Chaudhary, N., Peck, M. J., Hoeng, J., Coggins, C. R., and Peitsch, M. C. (2012) Genomic impact of cigarette smoke, with application to three smoking-related diseases Crit. Rev. Toxicol. 42, 877-889
29. Pogribny, I. P. and Rusyn, I. (2013) Environmental toxicants, epigenetics, and cancer Adv. Exp. Med. Biol. 754, 215-232
30. Hoeng, J., Talikka, M., Martin, F., Ansari, S., Drubin, D., Elamin, A., Gebel, S., Ivanov, N. V., Kenny, R. D., Koegel, U., Schlage, W. K., Sewer, A., Sierro, N., Thomson, T., and Peitsch, M. C. (2014) Toxicopanomics: applications of genomics, transcriptomics, proteomics and lipidomics in predictive mechanistic toxicology, in Principle and Methods on Toxicology (Hayes, A. W., Ed.) CRC Press, Boca Raton, FL, in press.
31. Dopazo, J. (2013) Genomics and transcriptomics in drug discovery. Drug Discovery Today [Online early access], DOI: 10.1016/j.drudis.2013.06.003, Published Online: June 15.
32. Schirmer, K., Fischer, B. B., Madureira, D. J., and Pillai, S. (2010) Transcriptomics in ecotoxicology Anal. Bioanal. Chem. 397, 917-923
33. Morris, M. K., Chi, A., Melas, I. N., and Alexopoulos, L. G. (2013) Phosphoproteomics in drug discovery. Drug Discovery Today [Online early access], DOI: 10.1016/j.drudis.2013.10.010, Published Online: Oct 18.
34. Bausch-Fluck, D., Hofmann, A., and Wollscheid, B. (2012) Cell surface capturing technologies for the surfaceome discovery of hepatocytes Methods Mol. Biol. 909, 1-16
35. Fiehn, O. (2001) Combining genomics, metabolome analysis, and biochemical modeling to understand metabolic networks Comp. Funct. Genomics 2, 155-168
36. James, L. P. (2013) Metabolomics: Integration of a new "omics" with clinical pharmacology Clin. Pharmacol. Ther. 94, 547-551
37. Gomez-Casati, D. F., Zanor, M. I., and Busi, M. V. (2013) Metabolomics in plants and humans: Applications in the prevention and diagnosis of diseases Biomed. Res. Int. 2013, 792527
38. Brennan, L. (2013) Metabolomics in nutrition research: Current status and perspectives Biomed. Soc. Trans. 41, 670-673
39. Robertson, D. G. (2005) Metabonomics in toxicology: A review Toxicol. Sci. 85, 809-822
40. Bouhifd, M., Hartung, T., Hogberg, H. T., Kleensang, A., and Zhao, L. (2013) Review: Toxicometabolomics J. Appl. Toxicol. 33, 1365-1383
41. Ramirez, T., Daneshian, M., Kamp, H., Bois, F. Y., Clench, M. R., Coen, M., Donley, B., Fischer, S. M., Ekman, D. R., Fabian, E., Guillou, C., Heuer, J., Hogberg, H. T., Jungnickel, H., Keun, H. C., Krennrich, G., Krupp, E., Luch, A., Noor, F., Peter, E., Riefke, B., Seymour, M., Skinner, N., Smirnova, L., Verheij, E., Wagner, S., Hartung, T., van Ravenzwaay, B., and Leist, M. (2013) Metabolomics in toxicology and preclinical research ALTEX 30, 209-225
42. Athersuch, T. J. (2012) The role of metabolomics in characterizing the human exposome Bioanalysis 4, 2207-2212
43. Rappaport, S. M., Li, H., Grigoryan, H., Funk, W. E., and Williams, E. R. (2012) Adductomics: Characterizing exposures to reactive electrophiles Toxicol. Lett. 213, 83-90
44. Dunn, W. B., Broadhurst, D. I., Atherton, H. J., Goodacre, R., and Griffin, J. L. (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy Chem. Soc. Rev. 40, 387-426
45. Jones, O. A., Swain, S. C., Svendsen, C., Griffin, J. L., Sturzenbaum, S. R., and Spurgeon, D. J. (2012) Potential new method of mixture effects testing using metabolomics and Caenorhabditis elegans J. Proteome Res. 11, 1446-1453
46. Shintu, L., Baudoin, R., Navratil, V., Prot, J. M., Pontoizeau, C., Defernez, M., Blaise, B. J., Domange, C., Péry, A. R., Toulhoat, P., Legallais, C., Brochot, C., Leclerc, E., and Dumas, M. E. (2012) Metabolomics-on-a-chip and predictive systems toxicology in microfluidic bioartificial organs Anal. Chem. 84, 1840-1848
47. Wenk, M. R. (2010) Lipidomics: New tools and applications Cell 143, 888-895
48. Vihervaara, T., Suoniemi, M., and Laaksonen, R. (2013) Lipidomics in drug discovery. Drug Discovery Today [Online early access], DOI: 10.1016/j.drudis.2013.09.008, Published Online: Sept 17.
49. Jung, H. R., Sylvanne, T., Koistinen, K. M., Tarasov, K., Kauhanen, D., and Ekroos, K. (2011) High throughput quantitative molecular lipidomics Biochim. Biophys. Acta 1811, 925-934
50. -/- mice in response to smoking cessation J. Liver 2, 1000132-1-1000132-11
51. Lu, S., Jessen, B., Strock, C., and Will, Y. (2012) The contribution of physicochemical properties to multiple in vitro cytotoxicity endpoints Toxicol. In Vitro 26, 613-620
52. Dondrup, M., Albaum, S. P., Griebel, T., Henckel, K., Jünemann, S., Kahlke, T., Kleindt, C. K., Küster, H., Linke, B., Mertens, D., Mittard-Runte, V., Neuweger, H., Runte, K. J., Tauch, A., Tille, F., Pühler, A., and Goesmann, A. (2009) EMMA 2- A MAGE-compliant system for the collaborative analysis and integration of microarray data BMC Bioinf. 10, 50-1-50-14
53. Gattiker, A., Hermida, L., Liechti, R., Xenarios, I., Collin, O., Rougemont, J., and Primig, M. (2009) MIMAS 3.0 is a multiomics information management and annotation system BMC Bioinf. 10, 151-1-151-8
54. Micheel, C., Nass, S., and Omenn, G. (2012) Evolution of translational omics: Lessons learned and the path forward, Institute of Medicine Consensus Report, National Academies Press, Washington, DC.
55. Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C. A., Causton, H. C., Gaasterland, T., Glenisson, P., Holstege, F. C., Kim, I. F., Markowitz, V., Matese, J. C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., and Vingron, M. (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data Nat. Genet. 29, 365-371
56. Rayner, T. F., Rocca-Serra, P., Spellman, P. T., Causton, H. C., Farne, A., Holloway, E., Irizarry, R. A., Liu, J., Maier, D. S., Miller, M., Petersen, K., Quackenbush, J., Sherlock, G., Stoeckert, C. J., Jr., White, J., Whetzel, P. L., Wymore, F., Parkinson, H., Sarkans, U., Ball, C. A., and Brazma, A. (2006) A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB BMC Bioinf. 7, 489-1-489-18
57. Taylor, C. F., Paton, N. W., Lilley, K. S., Binz, P. A., Julian, R. K., Jr., Jones, A. R., Zhu, W., Apweiler, R., Aebersold, R., Deutsch, E. W., Dunn, M. J., Heck, A. J., Leitner, A., Macht, M., Mann, M., Martens, L., Neubert, T. A., Patterson, S. D., Ping, P., Seymour, S. L., Souda, P., Tsugita, A., Vandekerckhove, J., Vondriska, T. M., Whitelegge, J. P., Wilkins, M. R., Xenarios, I., Yates, J. R., 3rd, and Hermjakob, H. (2007) The minimum information about a proteomics experiment (MIAPE) Nat. Biotechnol. 25, 887-893
58. Goecks, J., Nekrutenko, A., and Taylor, J. (2010) Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences Genome Biol. 11, R86-1-R86-13
59. Hull, D., Wolstencroft, K., Stevens, R., Goble, C., Pocock, M. R., Li, P., and Oinn, T. (2006) Taverna: A tool for building and running workflows of services Nucleic Acids Res. 34, W729-732
60. Saltz, J., Oster, S., Hastings, S., Langella, S., Kurc, T., Sanchez, W., Kher, M., Manisundaram, A., Shanbhag, K., and Covitz, P. (2006) caGrid: Design and implementation of the core architecture of the cancer biomedical informatics grid Bioinformatics 22, 1910-1916
61. Kavlock, R. J., Ankley, G., Blancato, J., Breen, M., Conolly, R., Dix, D., Houck, K., Hubal, E., Judson, R., Rabinowitz, J., Richard, A., Setzer, R. W., Shah, I., Villeneuve, D., and Weber, E. (2007) Computational toxicology- A state of the science mini review Toxicol. Sci. 103, 14-27
62. Dix, D. J., Houck, K. A., Martin, M. T., Richard, A. M., Setzer, R. W., and Kavlock, R. J. (2007) The ToxCast program for prioritizing toxicity testing of environmental chemicals Toxicol. Sci. 95, 5-12
63. Kavlock, R., Chandler, K., Houck, K., Hunter, S., Judson, R., Kleinstreuer, N., Knudsen, T., Martin, M., Padilla, S., Reif, D., Richard, A., Rotroff, D., Sipes, N., and Dix, D. (2012) Update on EPA's ToxCast program: Providing high throughput decision support tools for chemical risk management Chem. Res. Toxicol. 25, 1287-1302
64. Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Sochaski, M. A., Rotroff, D. M., Freeman, K., Clewell, H. J., 3rd., Dix, D. J., Andersen, M. E., Houck, K. A., Allen, B., Judson, R. S., Singh, R., Kavlock, R. J., Richard, A. M., and Thomas, R. S. (2012) Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment Toxicol. Sci. 125, 157-174
65. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M. (2012) KEGG for integration and interpretation of large-scale molecular data sets Nucleic Acids Res. 40, D109-114
66. Pico, A. R., Kelder, T., van Iersel, M. P., Hanspers, K., Conklin, B. R., and Evelo, C. (2008) WikiPathways: Pathway editing for the people PLoS Biol. 6, e184-1-e184-4
67. Xing, H., McDonagh, P. D., Bienkowska, J., Cashorali, T., Runge, K., Miller, R. E., Decaprio, D., Church, B., Roubenoff, R., Khalil, I. G., and Carulli, J. (2011) Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis PLoS Comput. Biol. 7, e1001105-1-e1001105-19
68. Xiang, Y., Kogel, U., Gebel, S., Peck, M. J., Peitsch, M. C., Akmaev, V. R., Hayete, B., Parikh, J., Young, L., Caprice, J., Hoeng, J., and Khalil, I. (2012) Discovery of emphysema/COPD-relevant molecular networks from an A/J mouse COPD inhalation study by reverse engineering and forward simulation (REFS™). German Conference on Bioinformatics, Jena, Germany, September 19-22.
69. Lecca, P. and Priami, C. (2013) Biological network inference for drug discovery Drug Discovery Today 18, 256-264
70. Gene Ontology Consortium (2013) Gene ontology annotations and resources Nucleic Acids Res. 41, D530-D535
71. Slater, T. and Song, D. H. (2012) Saved by the BEL: Ringing in a common language for the life sciences Drug Discovery World 75-80
72. Demir, E., Cary, M. P., Paley, S., Fukuda, K., Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., Luciano, J., Schacherer, F., Martinez-Flores, I., Hu, Z., Jimenez-Jacinto, V., Joshi-Tope, G., Kandasamy, K., Lopez-Fuentes, A. C., Mi, H., Pichler, E., Rodchenkov, I., Splendiani, A., Tkachev, S., Zucker, J., Gopinath, G., Rajasimha, H., Ramakrishnan, R., Shah, I., Syed, M., Anwar, N., Babur, O., Blinov, M., Brauner, E., Corwin, D., Donaldson, S., Gibbons, F., Goldberg, R., Hornbeck, P., Luna, A., Murray-Rust, P., Neumann, E., Ruebenacker, O., Samwald, M., van Iersel, M., Wimalaratne, S., Allen, K., Braun, B., Whirl-Carrillo, M., Cheung, K. H., Dahlquist, K., Finney, A., Gillespie, M., Glass, E., Gong, L., Haw, R., Honig, M., Hubaut, O., Kane, D., Krupa, S., Kutmon, M., Leonard, J., Marks, D., Merberg, D., Petri, V., Pico, A., Ravenscroft, D., Ren, L., Shah, N., Sunshine, M., Tang, R., Whaley, R., Letovksy, S., Buetow, K. H., Rzhetsky, A., Schachter, V., Sobral, B. S., Dogrusoz, U., McWeeney, S., Aladjem, M., Birney, E., Collado-Vides, J., Goto, S., Hucka, M., Le Novère, N., Maltsev, N., Pandey, A., Thomas, P., Wingender, E., Karp, P. D., Sander, C., and Bader, G. D. (2010) The BioPAX community standard for pathway data sharing Nat. Biotechnol. 28, 935-942
73. Westra, J. W., Schlage, W. K., Frushour, B. P., Gebel, S., Catlett, N. L., Han, W., Eddy, S. F., Hengstermann, A., Matthews, A. L., Mathis, C., Lichtner, R. B., Poussin, C., Talikka, M., Veljkovic, E., Van Hoose, A. A., Wong, B., Maria, M. J., Peitsch, M. C., Keeney, R. D., and Hoeng, J. (2011) Construction of a computable cell proliferation network focused on non-diseased lung cells BMC Syst. Biol. 5, 105-1-105-16
74. Schlage, W. K., Westra, J. W., Gebel, S., Catlett, N. L., Mathis, C., Frushour, B. P., Hengstermann, A., Van Hooser, A., Poussin, C., Wong, B., Lietz, M., Park, J., Drubin, D., Veljkovic, E., Peitsch, M. C., Hoeng, J., and Deehan, R. (2011) A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue BMC Syst. Biol. 5, 168-1-168-15
75. Gebel, S., Lichtner, R., Frushour, B., Schlage, W., Hoang, V., Talikka, M., Hengstermann, A., Mathis, C., Veljkovic, E., Müller, T., Peck, M., Peitsch, M. C., Deehan, R., Hoeng, J., and Westra, J. (2013) Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence Bioinf. Biol. Insights 7, 97-117
76. Hoeng, J., Park, J. S., Schlage, W. K., Frushour, B. P., Talikka, M., Toedter, G., Gebel, S., Deehan, R., Veljkovic, E., Westra, J. W., Peck, M. J., Boué, S., Kogel, U., Gonzalez-Suarez, I., Hengstermann, A., and Peitsch, M. C. (2013) Construction of a computable network model of tissue repair and angiogenesis in the lung J. Clin. Toxicol. S12-002-1-S12-002-14
77. Westra, J. W., Schlage, W. K., Hengstermann, A., Gebel, S., Mathis, C., Thomson, T., Wong, B., Hoang, V., Veljkovic, E., Peck, M., Lichtner, R. B., Weisensee, D., Talikka, M., Deehan, R., Hoeng, J., and Peitsch, M. C. (2013) A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue Bioinf. Biol. Insights 7, 167-192
78. Catlett, N. L., Bargnesi, A. J., Ungerer, S., Seagaran, T., Ladd, W., Elliston, K. O., and Pratt, D. (2013) Reverse causal reasoning: Applying qualitative causal knowledge to the interpretation of high-throughput data BMC Bioinf. 14, 340-1-340-14
79. Belcastro, V., Poussin, C., Gebel, S., Mathis, C., Schlage, W., Lichtner, R., Quadt-Humme, S., Wagner, S., Hoeng, J., and Peitsch, M. C. (2013) Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset Bioinf. Biol. Insights 7, 217-230
80. Kitano, H., Ghosh, S., and Matsuoka, Y. (2011) Social engineering for virtual 'big science' in systems biology Nat. Chem. Biol. 7, 323-326
81. Kelder, T., van Iersel, M. P., Hanspers, K., Kutmon, M., Conklin, B. R., Evelo, C. T., and Pico, A. R. (2012) WikiPathways: Building research communities on biological pathways Nucleic Acids Res. 40, D1301-D1307
82. The sbv IMPROVER project team (in alphabetical order) Ansari, S., Di Fabio, A., Binder, J., Boué, S., Hayes, W., Hoeng, J., Iskandar, A., Kleiman, R., Norel, R., O'Neel, B., Peitsch, M. C., Poussin, C., Pratt, D., Rhrissorrakai, K., Schlage, W. K., Stolovitzky, G., and Talikka, M. (2013) On crowd-verification of biological networks Bioinform. Biol. Insight 7, 1-19
83. UniProt Consortium (2013) Update on activities at the Universal Protein Resource (UniProt) in 2013 Nucleic Acids Res. 41, D43-D47
84. Kiyosawa, N., Manabe, S., Yamoto, T., and Sanbuissho, A. (2010) Practical application of toxicogenomics for profiling toxicant-induced biological perturbations Int. J. Mol. Sci. 11, 3397-3412
85. Hoeng, J., Kenney, R. D., Pratt, D., Martin, F., Sewer, A., Thomson, T. M., Drubin, D. A., Waters, C. A., de Graaf, D., and Peitsch, M. C. (2012) A network-based approach to quantify the impact of biologically active substances Drug Discovery Today 17, 413-418
86. Martin, F., Thomson, T. M., Sewer, A., Drubin, D. A., Mathis, C., Weisensee, D., Pratt, D., Hoeng, J., and Peitsch, M. C. (2012) Assessment of network perturbation amplitude by applying high-throughput data to causal biological networks BMC Syst. Biol. 6, 54-1-54-18
87. Iskandar, A. R., Martin, F., Talikka, M., Schlage, W. K., Kostadinova, R., Mathis, C., Hoeng, J., and Peitsch, M. C. (2013) Systems approaches evaluating the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues BioMed. Res. Int. 512086-1-512086-14
88. Thomson, T. M., Sewer, A., Martin, F., Belcastro, V., Frushour, B., Gebel, S., Park, J., Schlage, W. K., Talikka, M., Vasilyev, D., Westra, J. W., Deehan, R., Hoeng, J., and Peitsch, M. C. (2013) Quantitative assessment of biological impact using transcriptomic data and mechanistic network models Toxicol. Appl. Pharmacol. 272, 863-878
89. Hoeng, J., Talikka, M., Martin, F., Sewer, A., Yang, X., Iskandar, A., Schlage, W., and Peitsch, M. C. (2013) Case study: The role of mechanistic network models in systems toxicology. Drug Discovery Today [Online early access], DOI: 10.1016/j.drudis.2013.07.023, Published Online: Aug 9.
90. Knudsen, T. B. and Kleinstreuerm, N. C. (2011) Disruption of embryonic vascular development in predictive toxicology Birth Defects Res., Part C 93, 312-323
91. Shoda, L., Kreuwel, H., Gadkar, K., Zheng, Y., Whiting, C., Atkinson, M., Bluestone, J., Mathis, D., Young, D., and Ramanujan, S. (2010) The type 1 diabetes PhysioLab platform: A validated physiologically based mathematical model of pathogenesis in the non-obese diabetic mouse Clin. Exp. Immunol. 161, 250-267
92. Chan, J. R., Vuillaume, G., Bever, C., Lebrun, S., Lietz, M., Steffen, Y., Stolle, K., Wahba, K., Wang, X., Moodie, S., Hoeng, J., Peitsch, M. C., and Powell, L. M. (2012) The Apoe-/- mouse PhysioLab® platform: A validated physiologically-based mathematical model of atherosclerotic plaque progression in the Apoe-/- mouse BioDiscovery 3, 2-1-2-13
93. Rullmann, J. A., Struemper, H., Defranoux, N. A., Ramanujan, S., Meeuwisse, C. M., and van Elsas, A. (2005) Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform Syst. Biol. 152, 256-262
94. Maxwell, G. and Mackay, C. (2008) Application of a systems biology approach to skin allergy risk assessment Altern. Lab. Anim. 36, 521-556
95. Howell, B. A., Yang, Y., Kumar, R., Woodhead, J. L., Harrill, A. H., Clewell, H. J., 3rd., Andersen, M. E., Siler, S. Q., and Watkins, P. B. (2012) In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsym: a mechanistic, mathematical model of DILI J. Pharmacokinet. Pharmacodyn. 39, 527-541
96. Bhattacharya, S., Shoda, L. K., Zhang, Q., Woods, C. G., Howell, B. A., Siler, S. Q., Woodhead, J. L., Yang, Y., McMullen, P., Watkins, P. B., and Andersen, M. E. (2012) Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches Front. Physiol. 3, 462-1-462-18
97. Breen, M., Villeneuve, D. L., Ankley, G. T., Bencic, D. C., Breen, M. S., Watanabe, K. H., Lloyd, A. L., and Conolly, R. B. (2013) Developing predictive approaches to characterize adaptive responses of the reproductive endocrine axis to aromatase inhibition: II. Computational modeling Toxicol. Sci. 133, 234-247
98. Schmidt, B. J., Casey, F. P., Paterson, T., and Chan, J. R. (2013) Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis BMC Bioinf. 14, 221-1-221-16