| Literature DB >> 25767696 |
Stephanie Boue1, Brett Fields2, Julia Hoeng1, Jennifer Park2, Manuel C Peitsch1, Walter K Schlage1, Marja Talikka1, Ilona Binenbaum3, Vladimir Bondarenko4, Oleg V Bulgakov5, Vera Cherkasova6, Norberto Diaz-Diaz7, Larisa Fedorova8, Svetlana Guryanova9, Julia Guzova10, Galina Igorevna Koroleva11, Elena Kozhemyakina10, Rahul Kumar12, Noa Lavid13, Qingxian Lu14, Swapna Menon15, Yael Ouliel13, Samantha C Peterson16, Alexander Prokhorov9, Edward Sanders17, Sarah Schrier18, Golan Schwaitzer Neta13, Irina Shvydchenko19, Aravind Tallam20, Gema Villa-Fombuena21, John Wu22, Ilya Yudkevich23, Mariya Zelikman24.
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.Entities:
Keywords: COPD; Chronic Obstructive Pulmonary Disease; crowd verification; crowdsourcing; jamboree; network model; online collaboration; signaling pathway
Year: 2015 PMID: 25767696 PMCID: PMC4350443 DOI: 10.12688/f1000research.5984.2
Source DB: PubMed Journal: F1000Res ISSN: 2046-1402