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References from Exploring synergies between plant metabolic modelling and machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Directions for research and training in plant omics: Big Questions and Big Data
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Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism
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Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments
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MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models
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Machine and deep learning meet genome-scale metabolic modeling
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Recent advances on constraint-based models by integrating machine learning
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Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters
2021 · External reference
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An integrated approach to characterize genetic interaction networks in yeast metabolism
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10.1093/pcp/pcw208 · 2017 · External reference
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A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems
2015 · External reference
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10.3389/fpls.2018.00884 · 2018 · External reference
Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models
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2019 · External reference
A genome-scale metabolic model of soybean (Glycine max) highlights metabolic fluxes in seedlings
10.1104/pp.19.00122 · 2019 · External reference
A mass and charge balanced metabolic model of Setaria viridis revealed mechanisms of proton balancing in C4 plants
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Responses to light intensity in a genome-scale model of rice metabolism
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Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica
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2015 · External reference
Reconstruction of Oryza sativa indica genome scale metabolic model and its responses to varying RuBisCO activity, light intensity, and enzymatic cost conditions
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Drought stress responses in context-specific genome-scale metabolic models of Arabidopsis thaliana
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Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis’s responses to abiotic stresses
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10.3389/fpls.2015.00142
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High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource
10.1073/pnas.1401329111 · 2014 · External reference
A diel flux balance model captures interactions between light and dark metabolism during day-night cycles in C3 and crassulacean acid metabolism leaves
10.1104/pp.113.234468 · 2014 · External reference
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C4GEM, a genome-scale metabolic model to study C4 plant metabolism
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Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development
10.1186/s12918-018-0574-x · 2018 · External reference
Modeling rice metabolism: From elucidating environmental effects on cellular phenotype to guiding crop improvement
10.3389/fpls.2016.01795 · 2016 · External reference
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Reconstructing genome-scale metabolic models with merlin
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Dynamic elementary mode modelling of non-steady state flux data
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Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks
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Machine learning framework for assessment of microbial factory performance
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Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction
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Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells
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An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features
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Machine learning based analyses on metabolic networks supports high-throughput knockout screens
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Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines
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Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
2016 · External reference
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Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data
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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
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Predicting gastrointestinal drug effects using contextualized metabolic models
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A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria
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A biochemically-interpretable machine learning classifier for microbial GWAS
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