Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems

[1]  Nicolas Fontaine,et al.  Flux prediction using artificial neural network (ANN) for the upper part of glycolysis , 2019, PloS one.

[2]  Zoran Nikoloski,et al.  iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models , 2016, Bioinform..

[3]  V. Hatzimanikatis,et al.  Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models , 2018, bioRxiv.

[4]  M. Savageau.  Michaelis-Menten mechanism reconsidered: implications of fractal kinetics. , 1995, Journal of theoretical biology.

[5]  Zak Costello,et al.  A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data , 2018, npj Systems Biology and Applications.

[6]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[7]  Christoph B. Messner,et al.  Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts , 2018, Cell systems.

[8]  Concha Bielza,et al.  A survey on multi‐output regression , 2015, WIREs Data Mining Knowl. Discov..

[9]  Athel Cornish-Bowden,et al.  One hundred years of Michaelis–Menten kinetics☆ , 2015 .

[10]  Giancarlo Mauri,et al.  Linking Alterations in Metabolic Fluxes with Shifts in Metabolite Levels by Means of Kinetic Modeling , 2016, WIVACE.

[11]  Claudio Angione,et al.  Machine and deep learning meet genome-scale metabolic modeling , 2019, PLoS Comput. Biol..

[12]  Chiara Damiani,et al.  Systems metabolomics: from metabolomic snapshots to design principles. , 2020, Current opinion in biotechnology.

[13]  Preetam Ghosh,et al.  Recent advances on constraint-based models by integrating machine learning. , 2019, Current opinion in biotechnology.

[14]  Miroslava Cuperlovic-Culf,et al.  Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling , 2018, Metabolites.

[15]  Giancarlo Mauri,et al.  Constraining Mechanism Based Simulations to Identify Ensembles of Parametrizations to Characterize Metabolic Features , 2016, CIBB.

[16]  J. Duyzer,et al.  Fluxes of chemically reactive species inferred from mean concentration measurements , 1997 .

[17]  Giancarlo Mauri,et al.  Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes , 2018, BMC Bioinformatics.

[18]  L. Petzold.  Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations , 1983 .