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

Boué, S and Fields, B and Hoeng, J and Park, J and Peitsch, MC and Schlage, WK and Talikka, M and Binenbaum, I and Bondarenko, V and Bulgakov, OV and Cherkasova, V and Diaz-Diaz, N and Fedorova, L and Guryanova, S and Guzova, J and Igorevna Koroleva, G and Kozhemyakina, E and Kumar, R and Lavid, N and Lu, Q and Menon, S and Ouliel, Y and Peterson, SC and Prokhorov, A and Sanders, E and Schrier, S and Schwaitzer Neta, G and Shvydchenko, I and Tallam, A and Villa-Fombuena, G and Wu, J and Yudkevich, I and Zelikman, M (2015) Enhancement of COPD biological networks using a web-based collaboration interface. F1000Research, 4. ISSN 2046-1402

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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 ( 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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Boué, S
Fields, B
Hoeng, J
Park, J
Peitsch, MC
Schlage, WK
Talikka, M
Binenbaum, I
Bondarenko, V
Bulgakov, OV
Cherkasova, V
Diaz-Diaz, N
Fedorova, L
Guryanova, S
Guzova, J
Igorevna Koroleva, G
Kozhemyakina, E
Kumar, R
Lavid, N
Lu, Q
Menon, S
Ouliel, Y
Peterson, SC
Prokhorov, A
Sanders, E
Schrier, S
Schwaitzer Neta, G
Shvydchenko, I
Tallam, A
Villa-Fombuena, G
Wu, J
Yudkevich, I
Zelikman, M
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 January 2015
Date Type: Publication
Journal or Publication Title: F1000Research
Volume: 4
DOI or Unique Handle: 10.12688/f1000research.5984.2
Refereed: Yes
ISSN: 2046-1402
Date Deposited: 08 Aug 2016 17:38
Last Modified: 30 Mar 2021 17:55


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