ArticlePLoS computational biology2019
Integration of single-cell RNA-seq data into population models to characterize 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 61 papers.
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Who cites it
61 citing papers in PubMed.
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Spatial FBA reveals heterogeneous Warburg niches in renal tumors and lactate consumption in colorectal cancer.NPJ systems biology and applications · 2026Article
- Digital twins for in vivo metabolic flux estimations in patients with brain cancer.Cell metabolism · 2026Article
- Hallmarks of disease: how tuned hierarchies of intelligent molecular neural networks (MNNs) may matter.Endocrine-related cancer · 2026Review
- Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers.Frontiers in pharmacology · 2026Article
- The Role of Metabolites in Cell-Cell Communication: A Review of Databases and Computational Tools.Cells · 2025Review
- Beyond Bulk Metabolomics: Emerging Technologies for Defining Cell-Type Specific Metabolic Pathways in Health and Disease.Biomolecules · 2025Review
- Mechanistically informed machine learning links non-canonical TCA cycle activity to Warburg metabolism and hallmarks of malignancy.PLoS computational biology · 2025Article
- Beyond the Warburg Effect: Modeling the Dynamic and Context-Dependent Nature of Tumor Metabolism.Cancers · 2025Article
- MEBOCOST maps metabolite-mediated intercellular communications using single-cell RNA-seq.Nucleic acids research · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Article
- UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale.Bioinformatics (Oxford, England) · 2025Article
- A physics informed neural network approach to quantify antigen presentation activities at single cell level using omics data.Research square · 2025Article
- Enhancing genome-scale metabolic models with kinetic data: resolving growth and citramalate production trade-offs inBioinformatics advances · 2025Article
- Advancements in Protein Kinase Inhibitors: From Discovery to Clinical Applications.Research (Washington, D.C.) · 2025Review
- Metabolic complementation between cells drives the evolution of tissues and organs.Biology letters · 2024Article
- Intratumoral Heterogeneity and Metabolic Cross-Feeding in a Three-Dimensional Breast Cancer Culture: An In Silico Perspective.International journal of molecular sciences · 2024Article
- Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective.Molecular cancer · 2024Review
- MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions.Briefings in bioinformatics · 2024Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.
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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.