ArticleScientific reports2021
Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Incidence of chemotherapy-related cardiac dysfunction in cancer patients.Clinical cardiology · 2024Pooled it
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models.Genome biology · 2025Article
- Metabolic Flux Analysis ofInternational journal of molecular sciences · 2024Article
- VirulentPred 2.0: An improved method for prediction of virulent proteins in bacterial pathogens.Protein science : a publication of the Protein Society · 2023Article
- Chemical genetic approaches for the discovery of bacterial cell wall inhibitors.RSC medicinal chemistry · 2023Review
- Recent advancement in targeted therapy and role of emerging technologies to treat cancer.Medical oncology (Northwood, London, England) · 2023Review
- SL-scan identifies synthetic lethal interactions in cancer using metabolic networks.Scientific reports · 2023Article
- Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.Computational and structural biotechnology journal · 2023Review
- Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer.Royal Society open science · 2022Article
- Recombinant COL6 α2 as a Self-Organization Factor That Triggers Orderly Nerve Regeneration Without Guidance Cues.Frontiers in cellular neuroscience · 2021Article
- Chemotherapy-Induced Cardiotoxicity: Mechanisms, Detection and Emerging Therapies in Cardio-Oncology.Discoveries (Craiova, Romania)Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.
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