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

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-06-06 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf127

Francisco Guil, José M García

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Abstract

Summary: 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.

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

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一种快速提取GEM模型必需和合成致死基因的方法。

摘要：探索和分类必要的和合成的致死性基因是开发有效和靶向治疗各种疾病的关键。这一努力取决于遗传最小切集，它在代谢工程中也很有用。对于遗传最小割集的计算，人们提出了不同的方法。然而，随着大量新模型的出现及其复杂性的增加，在这一领域引入新的算法变得至关重要。本文提出了一种新的遗传最小割集计算方法，该方法利用线性规划技术来提高时间效率。该方法的关键概念是使用一个具有k个代表性的子集，将目标集替换为一个较小但具有代表性的子集。我们从运行时间的角度分析了它与gMCSPy的效率，gMCSPy是最近发表的计算遗传最小割集的研究。可用性和实现：软件和其他材料可在https://github.com/biogacop/fastMethod免费获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量