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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.

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Summary of root mean squared error (RMSE) from 20 case studies: averaged flux from 13C-MFA dataset, ML-only, and MFlux (ML + quadratic programming).The average RMSE is 7.7 from ML-only, and 5.6 from MFlux. Detailed information is in S1 and S2 Tables.
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pcbi.1004838.g007: Summary of root mean squared error (RMSE) from 20 case studies: averaged flux from 13C-MFA dataset, ML-only, and MFlux (ML + quadratic programming).The average RMSE is 7.7 from ML-only, and 5.6 from MFlux. Detailed information is in S1 and S2 Tables.

Mentions: To demonstrate the functionality of MFlux, we carried out tests on 20 cases, and the results are illustrated in Fig 7. Brief information for each case is listed in Table 1, and comprehensive results are included in S1 and S2 Tables. In general, MFlux can achieve decent flux predictions. Here we demonstrate two cases which are Cases 8 and 16.


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)

Summary of root mean squared error (RMSE) from 20 case studies: averaged flux from 13C-MFA dataset, ML-only, and MFlux (ML + quadratic programming).The average RMSE is 7.7 from ML-only, and 5.6 from MFlux. Detailed information is in S1 and S2 Tables.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004838.g007: Summary of root mean squared error (RMSE) from 20 case studies: averaged flux from 13C-MFA dataset, ML-only, and MFlux (ML + quadratic programming).The average RMSE is 7.7 from ML-only, and 5.6 from MFlux. Detailed information is in S1 and S2 Tables.
Mentions: To demonstrate the functionality of MFlux, we carried out tests on 20 cases, and the results are illustrated in Fig 7. Brief information for each case is listed in Table 1, and comprehensive results are included in S1 and S2 Tables. In general, MFlux can achieve decent flux predictions. Here we demonstrate two cases which are Cases 8 and 16.

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