摘要：SummaryMachine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM ofSynechococcussp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis,k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.Graphical AbstractDisplay OmittedHighlights•A pipeline for metabolic modeling inSynechococcussp. PCC 7002 is presented•Metabolic fluxes display clear differences in pathway activity across conditions•Omic-informed GSMMs provide critical mechanistic details within machine learning•Combining GSMM and machine learning improves methods based on transcriptomics aloneMetabolic Engineering; In Silico Biology; Artificial Intelligence; Bioengineering
