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.

Mechanistic Evaluation of the Impact of Smoking and Chronic Obstructive Pulmonary Disease on the Nasal Epithelium

“… 36 – 38 , 41 The original networks were modified to include COPD-relevant processes as well as to aggregate some of the smaller network models into coherent representations of biological processes. 40 , 42 The network models were chosen based on the assumption that most of the scraped nasal cells were epithelial cells 43 and that biological processes were similar to those found in the lower airways. The network models used are listed in Supplementary Table 1 , the sample details are shown in Supplementary Table 2 , and the network scoring is described in Figure 1A and B .…”

Systems Toxicology: Real World Applications and Opportunities

“…13 This is the basic goal of Systems Toxicology: Computational network models, 14 e.g., virtual organ models, 15 based on our pathway knowledge make predictions about organs or whole organism responses (such as the virtual embryo palate closure model). 16 Comparison of model outputs with actual measurements will then show how close we are to understanding the characteristics of living networked systems.…”

Dual Effect of Low-Molecular-Weight Bioregulators of Bacterial Origin in Experimental Model of Asthma

“…In addition, the diversity of the intestinal microflora at an early stage of life is a prerequisite for the development of the immune system, significantly reducing the frequency of asthma and other allergic diseases [11][12][13][14], and the use of broad-spectrum antibiotics in childhood significantly increases the likelihood of allergic asthma in adulthood [4,[15][16][17]. The manifestation of asthma and atopy is also influenced by a genetic predisposition: in the study of single-nucleus polymorphisms (SNPs) of 3062 children with asthma, multiple single SNPs were found, including the TLR and NLR genes, as well as significant interactions between these genes associated with phenotypic manifestations of asthma [18,19]. The revealed polymorphisms of the TLR and NLR genes affect the binding of these receptors to their ligands and to numerous adapter proteins, modifying intracellular signaling pathways involved during asthma, chronic obstructive pulmonary disease, and others [19,20].…”