Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.
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.
Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.
Stephen Gang
SG
Wu
Department of Computer Science and Engineering, Ohio State University, Columbus, Ohio, United States of America.
Yuxuan
Y
Wang
Boxed Wholesale, Edison, New Jersey, United States of America.
Wu
W
Jiang
Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.
Tolutola
T
Oyetunde
State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, People's Republic of China.
Ruilian
R
Yao
State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, People's Republic of China.
Xuehong
X
Zhang
Institute of Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Kazuyuki
K
Shimizu
Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America.
Yinjie J
YJ
Tang
Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America.
Forrest Sheng
FS
Bao
D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D002247:Carbon Isotopes / Q000378:metabolism; D019295:Computational Biology; D003663:Decision Trees; D000069550:Machine Learning; D064688:Metabolic Flux Analysis / Q000379:methods* / Q000706:statistics & numerical data; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D060388:Support Vector Machine; D049490:Systems Biology
D002247:Carbon Isotopes