A fast method for extracting essential and synthetic lethality genes in GEM models

TL;DR

This paper introduces a faster algorithm for identifying essential and synthetic lethality genes in genetic models, using linear programming to improve efficiency.

Contribution

A novel algorithm using linear programming and k-representative subsets to compute genetic minimal cut sets more efficiently.

Findings

The new algorithm improves temporal efficiency for computing genetic minimal cut sets.

The method was benchmarked against gMCSPy and showed better performance in terms of running time.

Abstract

Exploring and categorizing essential and synthetic lethality genes is crucial in developing effective and targeted therapies for various diseases. This endeavor hinges upon genetic minimal cut sets, which also find utility in metabolic engineering. Different methods have been suggested for calculating genetic minimal cut sets. Still, with the emergence of numerous new models and their increasing complexity, it has become essential to introduce new algorithms in this field. This paper presents a new algorithmic approach for computing genetic minimal cut sets, which utilizes linear programming techniques to improve temporal efficiency. The key concept of the method is to use a k-representative subset to replace the target set with a smaller, yet representative, one. We have analyzed its efficiency in terms of running times compared to gMCSPy, the most recent published research on computing genetic minimal cut sets.

Software and additional material are freely available at https://github.com/biogacop/fastMethod

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