Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning...

Area of Science:

• Computational Biology
• Network Science
• Systems Biology

Background:

• Protein-protein interaction networks (PPINs) are crucial for understanding biological processes.
• Static models of PPINs offer insights but cannot capture dynamic and cooperative protein complex behavior.
• Mathematical modeling of molecular relationships is essential for deeper biological understanding.

Purpose of the Study:

• To review the evolution of PPIN analysis from static to dynamic frameworks.
• To discuss challenges and advancements in dynamic PPIN modeling, including interpolation and centrality measures.
• To highlight machine learning and deep learning applications in predicting interactions and reconstructing protein complexes.

Main Methods:

• Introduction to graph and hypergraph theory, focusing on centrality measures.
• Review of dynamic graph- and hypergraph-based frameworks for PPIN analysis.
• Exploration of machine learning and deep learning approaches integrating diverse biological data.

Main Results:

• Dynamic PPIN modeling offers a more comprehensive view of protein complex behavior.
• Advanced network models can represent multi-node relationships essential for biological functions.
• Machine learning and deep learning enhance the prediction of novel interactions and transient complexes.

Conclusions:

• Dynamic PPIN modeling, combined with experimental validation, provides an integrated framework for understanding cellular functions.
• This approach supports the elucidation of coordinated protein functions across biological systems.
• The findings are valuable for advancing drug development strategies.