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

Area of Science:

• Systems Biology
• Computational Biology
• Pulmonary Medicine

Background:

• Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory airway disease with limited therapeutic options.
• Understanding early disease mechanisms is crucial for developing effective interventions.
• Biological network models offer a holistic approach to comprehend complex disease processes.

Purpose of the Study:

• To construct comprehensive biological network models for lung biology and early COPD.
• To leverage integrative systems biology and collaborative crowdsourcing for network development and verification.
• To provide a resource for understanding COPD pathobiology and identifying potential therapeutic targets.

Main Methods:

• Combined traditional literature curation with a data-driven approach using transcriptomics data.
• Developed an initial COPD network model set based on a non-diseased lung-relevant model.
• Utilized a crowdsourcing website for collaborative refinement and verification of biological networks by researchers.
• Conducted expert discussions and refinements during an in-person jamboree meeting.

Main Results:

• Successfully constructed 50 biological network models relevant to lung biology and early COPD.
• Demonstrated the refinement of specific networks (Neutrophil Signaling, Macrophage Signaling, Th1-Th2 Signaling) through the crowdsourcing approach.
• Created a publicly accessible resource (https://bionet.sbvimprover.com/) for browsing and downloading the COPD-relevant networks.

Conclusions:

• The integrative systems biology and crowdsourcing approach is effective for constructing accurate and comprehensive biological networks.
• The developed COPD network models provide valuable insights into lung pathobiology.
• These models can serve as a foundation for future research into COPD mechanisms and therapeutic strategies.