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Version 2. F1000Res. 2015 Jan 29 [revised 2015 May 20];4:32. doi: 10.12688/f1000research.5984.2. eCollection 2015.

Enhancement of COPD biological networks using a web-based collaboration interface.

Author information

1
Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland.
2
Selventa, One Alewife Center, Cambridge, MA, 02140, USA.
3
Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece.
4
Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA.
5
University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
6
Private, Washington DC, USA.
7
Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain.
8
University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA.
9
Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation.
10
Private, Boston, MA, USA.
11
USAMRIID, Attn: MCMR-UIZ-R, 1425 Porter Street, Frederick, MD, 21702-5011, USA.
12
Institute of Microbial Technology, Chandigarh, 160036, India.
13
Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel.
14
Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA.
15
AnalyzeDat Consulting Services, Ernakulam, India.
16
Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA.
17
Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland.
18
Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
19
Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation.
20
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg.
21
Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain.
22
Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA.
23
University of Manchester, Oxford Rd, Manchester, M13 9PL, UK.
24
University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA.

Abstract

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

KEYWORDS:

COPD; Chronic Obstructive Pulmonary Disease; crowd verification; crowdsourcing; jamboree; network model; online collaboration; signaling pathway

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