Heuristic shortest hyperpaths in cell signaling hypergraphs.

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Cell signaling pathways are fundamental to systems biology, involving complex reaction sequences.
• Modeling these pathways requires directed hypergraphs, and inferring the most parsimonious pathway is an NP-complete problem.
• Current methods for shortest hyperpaths in signaling networks are computationally intensive and often restricted to acyclic pathways.

Purpose of the Study:

• To develop a novel heuristic for finding shortest hyperpaths in directed hypergraphs that can handle cycles.
• To provide an efficient and accurate method for inferring cell signaling pathways.
• To make the analysis of complex, potentially cyclic signaling networks computationally tractable.

Main Methods:

• Development of a new heuristic algorithm for general shortest hyperpaths.
• Implementation of a practical algorithm for generating all source-sink hyperpaths.
• Comprehensive experimental validation using NCI-PID and Reactome pathway databases.

Main Results:

• The heuristic efficiently handles cycles and guarantees performance.
• It finds provably optimal hyperpaths for singleton-tail hypergraphs.
• The heuristic accurately matches state-of-the-art methods on acyclic instances (>99%) and outperforms them on cyclic instances where no prior solution existed.

Conclusions:

• The developed shortest hyperpath heuristic is both fast and accurate.
• This advancement enables practical inference of source-sink hyperpaths in real-world cell signaling networks, including those with cycles.