Machine and deep learning meet genome-scale metabolic modeling
Fig 3
Multiomic data analysis by combination of constraint-based modeling with machine learning.
(a) Fluxomic analysis involves FBA or related techniques performed on a general-purpose GSMM, from which the flux data obtained can be used as input for unsupervised or supervised machine learning. (b) To improve the accuracy of machine learning predictions, multiomic datasets are obtained using high-throughput analytics—e.g., transcriptomics (DNA microarrays, RNA sequencing), proteomics (2D gel electrophoresis, stable isotope labeling, mass spectrometry), or metabolomics (NMR spectroscopy, isotopic labeling, LC-MS, GC-MS). As these datasets are obtained from different sources, they must undergo several preprocessing stages such as filtration and normalization to maintain synchronicity, account for variance, and reduce noise. Condition-specific knowledge-based models are generated by introducing these multiple datasets into GSMMs to obtain more precise flux estimations, from which machine learning techniques can be applied to infer biologically relevant patterns in the data. (c) Alternatively, machine learning can be directly applied to single- or multiomic datasets to produce or improve GSMMs or fluxomic data. FBA, flux balance analysis; GC-MS, gas chromatography–mass spectroscopy; GSMM, genome-scale metabolic model; LC-MS, liquid chromatography–mass spectroscopy; NMR, nuclear magnetic resonance.