Limits...
Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

Wu SG, Wang Y, Jiang W, Oyetunde T, Yao R, Zhang X, Shimizu K, Tang YJ, Bao FS - PLoS Comput. Biol. (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.

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.

Show MeSH

Related in: MedlinePlus

A comparison of the 13C-MFA flux, the flux predicted by ML only, and the flux predicted by MFlux in Case 16.G. thermoglucosidasius M10EXG was incubated in sealed bottles (micro-aerobic condition), supplied with glucose as a carbon source. Detailed information is in S2 Table.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4836714&req=5

pcbi.1004838.g009: A comparison of the 13C-MFA flux, the flux predicted by ML only, and the flux predicted by MFlux in Case 16.G. thermoglucosidasius M10EXG was incubated in sealed bottles (micro-aerobic condition), supplied with glucose as a carbon source. Detailed information is in S2 Table.

Mentions: In Case 16, G. thermoglucosidasius strain M10EXG grows under microaerobic conditions. G. thermoglucosidasius is a thermophilic and ethanol tolerant bacterium which can convert both hexose and pentose into ethanol [28]. To predict its central fluxomes, the parameter set used is listed in S1 Table, along with the default boundary settings for flux correction. A heat map (Fig 9) visualizes 13C-MFA fluxes with ML-only fluxes and MFlux results. The results are encouraging: ML-only prediction gives an RMSE of 4.0, while MFlux uses both ML and quadratic programming to improve the prediction to an RMSE of only 3.0. Among the 20 case studies, the average flux set has very large variations (RMSE of 33.5) from actual 13C-MFA fluxes (S2 Table). In this case, MFlux reduces the deviations of predicted fluxes from 13C-MFA values.


Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

Wu SG, Wang Y, Jiang W, Oyetunde T, Yao R, Zhang X, Shimizu K, Tang YJ, Bao FS - PLoS Comput. Biol. (2016)

A comparison of the 13C-MFA flux, the flux predicted by ML only, and the flux predicted by MFlux in Case 16.G. thermoglucosidasius M10EXG was incubated in sealed bottles (micro-aerobic condition), supplied with glucose as a carbon source. Detailed information is in S2 Table.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836714&req=5

pcbi.1004838.g009: A comparison of the 13C-MFA flux, the flux predicted by ML only, and the flux predicted by MFlux in Case 16.G. thermoglucosidasius M10EXG was incubated in sealed bottles (micro-aerobic condition), supplied with glucose as a carbon source. Detailed information is in S2 Table.
Mentions: In Case 16, G. thermoglucosidasius strain M10EXG grows under microaerobic conditions. G. thermoglucosidasius is a thermophilic and ethanol tolerant bacterium which can convert both hexose and pentose into ethanol [28]. To predict its central fluxomes, the parameter set used is listed in S1 Table, along with the default boundary settings for flux correction. A heat map (Fig 9) visualizes 13C-MFA fluxes with ML-only fluxes and MFlux results. The results are encouraging: ML-only prediction gives an RMSE of 4.0, while MFlux uses both ML and quadratic programming to improve the prediction to an RMSE of only 3.0. Among the 20 case studies, the average flux set has very large variations (RMSE of 33.5) from actual 13C-MFA fluxes (S2 Table). In this case, MFlux reduces the deviations of predicted fluxes from 13C-MFA values.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.

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.

Show MeSH
Related in: MedlinePlus