ArticlePLoS computational biology2016
Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Metabolic engineering strategies: utilizing different microbial strains and advanced technologies for the synthesis of high-valued terpenoids.Antonie van Leeuwenhoek · 2026Review
- Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.Machine learning · 2025Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
- FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis.ACS synthetic biology · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- Biotechnological production of omega-3 fatty acids: current status and future perspectives.Frontiers in microbiology · 2023Review
- Synthetic Biology Meets Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Optimization and Scale-Up of Fermentation Processes Driven by Models.Bioengineering (Basel, Switzerland) · 2022Review
- Recent advances in proteomics and metabolomics in plants.Molecular horticulture · 2022Review
- Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges.Biomolecules · 2022Review
- Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.Frontiers in artificial intelligence · 2022Article
- Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022Review
- Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.Metabolic engineering communications · 2020Review
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
- Machine and deep learning meet genome-scale metabolic modeling.PLoS computational biology · 2019Review
- Article
- Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine.BioMed research international · 2019Review
Corrections and comments
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.
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