ArticlePLoS computational biology2019
A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Spatial FBA reveals heterogeneous Warburg niches in renal tumors and lactate consumption in colorectal cancer.NPJ systems biology and applications · 2026Article
- Epigenetic control of metabolic identity across cell types.BMC genomics · 2025Article
- Metabolic Flux Analysis ofInternational journal of molecular sciences · 2024Article
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
- SL-scan identifies synthetic lethal interactions in cancer using metabolic networks.Scientific reports · 2023Article
- How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?Biotechnology and bioengineering · 2023Article
- Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.NPJ systems biology and applications · 2023Article
- Genetically personalised organ-specific metabolic models in health and disease.Nature communications · 2022Article
- Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.BMC bioinformatics · 2022Article
- INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.PLoS computational biology · 2022Article
- Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases.Frontiers in cardiovascular medicine · 2022Review
- Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.Computational and structural biotechnology journal · 2022Article
- Article
- Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.Frontiers in oncology · 2022Review
- Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme.International journal of molecular sciences · 2021Review
- Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment.Cancers · 2021Review
- Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.Journal of personalized medicine · 2021Article
- Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models.BMC bioinformatics · 2021Article
- On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples.Current genomics · 2021Article
- Towards the routine use of in silico screenings for drug discovery using metabolic modelling.Biochemical Society transactions · 2020Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.
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