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

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.

Integrated approaches in diagnostics and therapy of allergic diseases

“…Для отображения более сложных связей между компонентами опи-сываемой системы создаются программные про-дукты, изобретаются новые языки программиро-вания [45], например, на основе языка OpenBEL (open-access biological expression language) [46][47][48]. Используя этот ресурс, фраза «IL18 повыша-ет экспрессию интерферона гамма» будет выра-жена формулой: p(HGNC:IL18) directlyIncreases p(HGNC:IFNG).…”

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL)

“…A BEL knowledgebase can be used to query, interpret, and analyse, and can be visualized as graphs (2, 23, 30, 31). Representing relationship information in a computable format serves systems biology and toxicology approaches, especially the network-based approaches that have emerged as powerful tools for interpreting high-throughput data (1, 32–36). …”

“…The networks represent causal signaling pathways across biological processes such as cell fate, cell stress, cell proliferation, inflammation, tissue repair, and angiogenesis in the pulmonary and vascular systems. Public manual curation is facilitated by Bionet, a cloud-based crowd verification portal in the frame of the sbvIMPROVER Network Verification Challenge (32). In the first challenge, 50 network models based on human non-diseased respiratory tissue, including biological processes such as cell stress, cell fate, cell proliferation, immune, and tissue response, were provided on the Bionet website (https://bionet.sbvimprover.com/) for the challenge participation.…”

Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

“…Since 2015, the CBN database website has provided free access to the full collection [27]. In addition to the original focus of inhalation toxicology covering the non-diseased respiratory tract tissues, causal networks for non-diseased vascular tissues, chronic obstructive pulmonary disease, and atherosclerosis plaque destabilization have been assembled and published to enrich the covered biological contexts [58][59][60].…”

“…This motivated additional and innovative crowdsourced verification initiatives to consolidate the accuracy of the biological mechanisms encoded in the networks. They took place in the framework of the network verification challenges of the systems biology verification initiative (sbvIMPROVER NVC) [59,[61][62][63]. These challenges were based on a novel crowdsourcing approach by a large community of more than 50 contributors who were given tools to vote on various edges and nodes of the causal networks via a dedicated web interface [64].…”