TY - JOUR
T1 - Enhancement of COPD biological networks using a web-based collaboration interface
AU - The sbv IMPROVER project team (in alphabetical order)
AU - Boué, Stéphanie
AU - Fields, Brett
AU - Hoeng, Julia
AU - Park, Jennifer
AU - Peitsch, Manuel C.
AU - Schlage, Walter K.
AU - Talikka, Marja
AU - Binenbaum, Ilona
AU - Bondarenko, Vladimir
AU - Bulgakov, Oleg V.
AU - Cherkasova, Vera
AU - Diaz-Diaz, Norberto
AU - Fedorova, Larisa
AU - Guryanova, Svetlana
AU - Guzova, Julia
AU - Igorevna Koroleva, Galina
AU - Kozhemyakina, Elena
AU - Kumar, Rahul
AU - Lavid, Noa
AU - Lu, Qingxian
AU - Menon, Swapna
AU - Ouliel, Yael
AU - Peterson, Samantha C.
AU - Prokhorov, Alexander
AU - Sanders, Edward
AU - Schrier, Sarah
AU - Schwaitzer Neta, Golan
AU - Shvydchenko, Irina
AU - Tallam, Aravind
AU - Villa-Fombuena, Gema
AU - Wu, John
AU - Yudkevich, Ilya
AU - Zelikman, Mariya
N1 - Publisher Copyright:
© 2015 The sbv IMPROVER project team (in alphabetical order) et al.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Chronic Obstructive Pulmonary Disease
KW - COPD
KW - Crowd verification
KW - Crowdsourcing
KW - Jamboree
KW - Network model
KW - Online collaboration
KW - Signaling pathway
UR - http://www.scopus.com/inward/record.url?scp=85023209652&partnerID=8YFLogxK
U2 - 10.12688/f1000research.5984.2
DO - 10.12688/f1000research.5984.2
M3 - Article
AN - SCOPUS:85023209652
SN - 2046-1402
VL - 4
JO - F1000Research
JF - F1000Research
M1 - 32
ER -