ArticleBioinformatics (Oxford, England)2024
gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.Briefings in bioinformatics · 2024Pooled it
- gMISpy: integration of complex regulatory networks and genome scale metabolic models.Bioinformatics (Oxford, England) · 2026Article
- Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention set.Bioinformatics advances · 2026Article
- Minimal cut sets in metabolic networks: from conceptual foundations to applications in metabolic engineering and biomedicine.Briefings in bioinformatics · 2025Review
- A fast method for extracting essential and synthetic lethality genes in GEM models.Bioinformatics advances · 2025Article
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Authors and funding
6 authors.
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Abstract
motivationThe identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software.
resultsHere, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. AVAILABILITY AND IMPLEMENTATION: The Python package gMCSpy and the data underlying this manuscript can be accessed at: https://github.com/PlanesLab/gMCSpy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.