A fast method for extracting essential and synthetic lethality genes in
GEM models
Abstract
The exploration and categorization of essential and synthetic lethality
genes hold significant importance in seeking effective and targeted
therapies for diverse ailments. This endeavor hinges upon genetic
minimal cut sets (gMCSs), which also find utility in metabolic
engineering. There have been various methods suggested for calculating
gMCSs. Still, with the emergence of numerous new models and their
growing intricacy, it has become vital to introduce new algorithms in
this field. This paper presents a new algorithmic approach for computing
gMCSs, 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 one.