ArticlePloS one2020
A comparison of Monte Carlo sampling methods for metabolic network models.
Article in PloS one, 2020. 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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20 citing papers in PubMed.
- Flux sampling and graph neural networks for improved gene essentiality prediction in mammalian genome-scale metabolic models.NPJ systems biology and applications · 2026Article
- Cancer-associated fibroblasts drive metabolic heterogeneity in colorectal cancer cells: predictions from metabolic modeling.NPJ systems biology and applications · 2026Article
- Evaluating plant growth-defense trade-offs by modeling the interaction between primary and secondary metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- HUMESS: integrating quantitative transcriptomic analysis and metabolic modeling to unveil condition-specific gene signatures.Bioinformatics (Oxford, England) · 2025Article
- Metabolic modeling of host-microbe interactions.Computational and structural biotechnology journal · 2025Review
- A hierarchy of metabolite exchanges in metabolic models of microbial species and communities.PLoS computational biology · 2024Article
- Genome scale metabolic network modelling for metabolic profile predictions.PLoS computational biology · 2024Article
- Flux sampling in genome-scale metabolic modeling of microbial communities.BMC bioinformatics · 2024Article
- LooplessFluxSampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models.BMC bioinformatics · 2024Article
- dingo: a Python package for metabolic flux sampling.Bioinformatics advances · 2024Article
- Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.iScience · 2023Article
- Practical sampling of constraint-based models: Optimized thinning boosts CHRR performance.PLoS computational biology · 2023Article
- Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose inBioengineering (Basel, Switzerland) · 2023Article
- Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities.bioRxiv : the preprint server for biology · 2023Article
- Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.bioRxiv : the preprint server for biology · 2023Article
- Meta-Dynamic Network Modelling for Biochemical Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Inspecting the Solution Space of Genome-Scale Metabolic Models.Metabolites · 2022Article
- Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.Frontiers in oncology · 2022Review
- Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts.Microbial cell factories · 2021Article
- Guided extraction of genome-scale metabolic models for the integration and analysis of omics data.Computational and structural biotechnology journal · 2021Article
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3 authors.
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Abstract
Reaction rates (fluxes) in a metabolic network can be analyzed using constraint-based modeling which imposes a steady state assumption on the system. In a deterministic formulation of the problem the steady state assumption has to be fulfilled exactly, and the observed fluxes are included in the model without accounting for experimental noise. One can relax the steady state constraint, and also include experimental noise in the model, through a stochastic formulation of the problem. Uniform sampling of fluxes, feasible in both the deterministic and stochastic formulation, can provide us with statistical properties of the metabolic network, such as marginal flux probability distributions. In this study we give an overview of both the deterministic and stochastic formulation of the problem, and of available Monte Carlo sampling methods for sampling the corresponding solution space. We apply the ACHR, OPTGP, CHRR and Gibbs sampling algorithms to ten metabolic networks and evaluate their convergence, consistency and efficiency. The coordinate hit-and-run with rounding (CHRR) is found to perform best among the algorithms suitable for the deterministic formulation. A desirable property of CHRR is its guaranteed distributional convergence. Among the three other algorithms, ACHR has the largest consistency with CHRR for genome scale models. For the stochastic formulation, the Gibbs sampler is the only method appropriate for sampling at genome scale. However, our analysis ranks it as less efficient than the samplers used for the deterministic formulation.
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