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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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Related in: MedlinePlus

A comparison of three ML algorithms: SVM, k-NN, and decision tree.The best cross-validation results on 29 fluxes are compared. All tests in this step were performed on the WT dataset only.
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pcbi.1004838.g004: A comparison of three ML algorithms: SVM, k-NN, and decision tree.The best cross-validation results on 29 fluxes are compared. All tests in this step were performed on the WT dataset only.

Mentions: To decide the most suitable ML algorithm, we first performed a grid search in the parameter space, using a dataset of wild type (WT) samples only. The best results of three different algorithms (for SVM, linear kernel only here) are presented in Fig 4. SVM makes better predictions than either the decision tree or k-NN on most fluxes. After this step, we carried out a second round of grid search to optimize parameters and improve the performance of SVM on the whole phenotype (WP) dataset (both WT and engineered). Both the linear kernel and radial bias function (RBF) kernel were included in this round of grid search.


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 three ML algorithms: SVM, k-NN, and decision tree.The best cross-validation results on 29 fluxes are compared. All tests in this step were performed on the WT dataset only.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004838.g004: A comparison of three ML algorithms: SVM, k-NN, and decision tree.The best cross-validation results on 29 fluxes are compared. All tests in this step were performed on the WT dataset only.
Mentions: To decide the most suitable ML algorithm, we first performed a grid search in the parameter space, using a dataset of wild type (WT) samples only. The best results of three different algorithms (for SVM, linear kernel only here) are presented in Fig 4. SVM makes better predictions than either the decision tree or k-NN on most fluxes. After this step, we carried out a second round of grid search to optimize parameters and improve the performance of SVM on the whole phenotype (WP) dataset (both WT and engineered). Both the linear kernel and radial bias function (RBF) kernel were included in this round of grid search.

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