ArticleOncotarget2018
Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Cancer-associated fibroblasts drive metabolic heterogeneity in colorectal cancer cells: predictions from metabolic modeling.NPJ systems biology and applications · 2026Article
- Multi-Omics Analysis Revealed Increased De Novo Synthesis of Serine and Lower Activity of the Methionine Cycle in Breast Cancer Cell Lines.Molecules (Basel, Switzerland) · 2023Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Reconstructed Genome-Scale Metabolic Model Characterizes Adaptive Metabolic Flux Changes in Peripheral Blood Mononuclear Cells in Severe COVID-19 Patients.International journal of molecular sciences · 2022Article
- Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.iScience · 2022Article
- Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development.Biomedicines · 2021Article
- A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19.Scientific reports · 2021Article
- Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors.Cell systems · 2021Article
- An integrated computational and experimental study to investigate Staphylococcus aureus metabolism.NPJ systems biology and applications · 2020Article
- Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches.BMC biology · 2019Review
- Dichloroacetate (DCA) and Cancer: An Overview towards Clinical Applications.Oxidative medicine and cellular longevity · 2019Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments.
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Registered trials
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.