Community-reviewed biological network models for toxicology and drug discovery applications

Gene Regulation and Systems Biology

Volumn 10, Issue , 2016, Pages 51-66

  Namasivayam, Aishwarya Alex ^a   Morales, Alejandro Ferreiro ^b   Lacave, Ángela María Fajardo ^b   Tallam, Aravind ^k   Simovic, Borislav ^j   Alfaro, David Garrido ^b   Bobbili, Dheeraj Reddy ^a   Martin, Florian ^c   Androsova, Ganna ^a   Shvydchenko, Irina ⁱ   Park, Jennifer ^g   Val Calvo, Jorge ^l   Hoeng, Julia ^c   Peitsch, Manuel C ^c   Racero, Manuel González Vélez ^b   Biryukov, Maria ^a   Talikka, Marja ^c   Pérez, Modesto Berraquero ^b   Rohatgi, Neha ^h   Díaz Díaz, Noberto ^b   Mandarapu, Rajesh ^e   Ruiz, Rubén Amián ^b   Davidyan, Sergey ^m   Narayanasamy, Shaman ^a   Boué, Stéphanie ^c   Guryanova, Svetlana ^d   Arbas, Susana Martínez ^a   Menon, Swapna ^f   Xiang, Yang ^c   more..

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^b UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

^c PHILIP MORRIS PRODUCTS S A   (Switzerland)

^d SHEMYAKIN OVCHINNIKOV INSTITUTE OF BIOORGANIC CHEMISTRY   (Russian Federation)

^e Prakhya Research Laboratories   (India)

^f AnalyzeDat Consulting Services   (India)

^g Selventa   (United States)

^h UNIVERSITY OF ICELAND   (Iceland)

ⁱ Kuban State University of Physical Culture Sport and Tourism   (Russian Federation)

^j FM Pharm Ltd   (Serbia)

^k INSTITUTE OF INFECTION IMMUNOLOGY   (Germany)

^l CSIC   (Spain)

^m EMANUEL INSTITUTE OF BIOCHEMICAL PHYSICS   (Russian Federation)

more..

Author keywords

Biological network; COPD; Crowdsourcing; Drug discovery; Toxicology

Indexed keywords

ARTICLE; BIOLOGICAL EXPRESSION LANGUAGE; BIOLOGICAL NETWORK MODEL; CHRONIC OBSTRUCTIVE LUNG DISEASE; COMPARATIVE STUDY; COMPUTER LANGUAGE; CONTROLLED STUDY; DRUG DEVELOPMENT; MODEL; QUANTITATIVE ANALYSIS; TOXICOLOGY; TRANSCRIPTOMICS;

EID: 84985904911     PISSN: None     EISSN: 11776250     Source Type: Journal    
DOI: 10.4137/GRSB.S39076     Document Type: Article

References (38)

1. (GOLD) GIfCOLD. From the Global Strategy for the Diagnosis, Management and Prevention of COPD. Global Initiative for Chronic Obstructive Lung Disease (GOLD); 2014. Available at: http://www.goldcopd.org/.
2. King PT. Inflammation in chronic obstructive pulmonary disease and its role in cardiovascular disease and lung cancer. Clin Transl Med. 2015;4(1):68.
3. Thorley AJ, Tetley TD. Pulmonary epithelium, cigarette smoke, and chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2007;2(4):409-28.
4. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27-30.
5. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42(Database issue):D199-D205.
6. D’Eustachio P. Reactome knowledgebase of human biological pathways and processes. Methods Mol Biol. 2011;694:49-61.
7. Croft D, O’Kelly G, Wu G, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39(Database issue):691-7.
8. sbv IMPROVER Project Team, Boue S, Fields B, et al. Enhancement of COPD biological networks using a web-based collaboration interface. F1000Res. 2015;4:32.
9. sbv IMPROVER Project Team, Boue S, Fields B, et al. Reputation-based collaborative network biology. Pac Symp Biocomput. 2015:270-81.
10. Westra JW, Schlage WK, Frushour BP, et al. Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC Syst Biol. 2011;5:105.
11. Schlage WK, Westra JW, Gebel S, et al. A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC Syst Biol. 2011;5:168.
12. Park JS, Schlage WK, Frushour BP, et al. Construction of a Computable Network Model of Tissue Repair and Angiogenesis in the Lung. J Clinic Toxicol. 2013:S:12.http://dx.doi.org/10.4172/2161-0495.S12-002.
13. Gebel S, Lichtner RB, Frushour B, et al. Construction of a computable network model for DNA damage, autophagy, cell death, and senescence. Bioinform Biol Insights. 2013;7:97-117.
14. Westra JW, Schlage WK, Hengstermann A, et al. A modular cell-type focused inflammatory process network model for non-diseased pulmonary tissue. Bioinform Biol Insights. 2013;7:167-92.
15. Prill RJ, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Stolovitzky G. Crowdsourcing network inference: the DREAM predictive signaling network challenge. Sci Signal. 2011;4(189):mr7.
16. Eiben CB, Siegel JB, Bale JB, et al. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nat Biotechnol. 2012;30(2):190-2.
17. Lee J, Kladwang W, Lee M, et al. RNA design rules from a massive open laboratory. Proc Natl Acad Sci U S A. 2014;111(6):2122-7.
18. Kawrykow A, Roumanis G, Kam A, et al. Phylo: a citizen science approach for improving multiple sequence alignment. PLoS One. 2012;7(3):e31362.
19. Loguercio S, Good BM, Su AI. Dizeez: an online game for human gene-disease annotation. PLoS One. 2013;8(8):e71171.
20. Vashisht R, Mondal AK, Jain A, et al. Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis. PLoS One. 2012;7(7):e39808.
21. Good BM, Nanis M, Wu C, Su AI. Microtask crowdsourcing for disease mention annotation in PubMed abstracts. Pac Symp Biocomput. 2015:282-93.
22. Martin F, Sewer A, Talikka M, Xiang Y, Hoeng J, Peitsch MC. Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models. BMC Bioinformatics. 2014;15:238.
23. Catlett NL, Bargnesi AJ, Ungerer S, et al. Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data. BMC Bioinformatics. 2013;14(1):340.
24. Ackermann M, Strimmer K. A general modular framework for gene set enrichment analysis. BMC Bioinformatics. 2009;10(1):47.
25. Subramanian A, Tamayo P, Mootha VK, 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(43):15545-50.
26. Hoeng J, Deehan R, Pratt D, et al. A network-based approach to quantifying the impact of biologically active substances. Drug Discov Today. 2012;17(9-10):413-8.
27. Martin F, Thomson TM, Sewer A, et al. Assessment of network perturbation amplitude by applying high-throughput data to causal biological networks. BMC Syst Biol. 2012;6(1):54.
28. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-9.
29. Narita M, Young AR, Arakawa S, et al. Spatial coupling of mTOR and autophagy augments secretory phenotypes. Science. 2011;332(6032):966-70.
30. Phillips B, Veljkovic E, Peck MJ, et al. 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. Food Chem Toxicol. 2015;80:328-45.
31. John-Schuster G, Hager K, Conlon TM, et al. Cigarette smoke-induced iBALT mediates macrophage activation in a B cell-dependent manner in COPD. Am J Physiol Lung Cell Mol Physiol. 2014;307(9):L692-706.
32. Boué S, Talikka M, Westra JW, et al. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database. 2015;2015:bav030.
33. Talikka M, Boue S, Schlage WK. Causal Biological Network Database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Comput Syst Toxicol. 2015;54:65-93.
34. Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C. WikiPathways: pathway editing for the people. PLoS Biol. 2008;6(7):e184.
35. Helikar T, Kowal B, McClenathan S, et al. The cell collective: toward an open and collaborative approach to systems biology. BMC Syst Biol. 2012;6:96.
36. Serrano M, Lin AW, McCurrach ME, Beach D, Lowe SW. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16INK4a. Cell. 1997;88(5):593-602.
37. Couillin I, Vasseur V, Charron S, et al. IL-1R1/MyD88 signaling is critical for elastase-induced lung inflammation and emphysema. J Immunol. 2009;183(12):8195-202.
38. Daliri S, Del Bosque D, Umer M, et al. A promoting role for MyD88/IRAK4 signaling in lung fibrosis during COPD progression. B37. Tell Me Why: COPD Pathogenesis. American Thoracic Society, Denver, Colorado; 2015:A2905-A2905. http://www.atsjournals.org/doi/book/10.1164/ajrccm-conference.2015.