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A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

ABSTRACT: Summary Machine 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 of Synechococcus sp. 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 Abstract Highlights • A pipeline for metabolic modeling in Synechococcus sp. 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 alone Metabolic Engineering; In Silico Biology; Artificial Intelligence; Bioengineering

SUBMITTER: Vijayakumar S

PROVIDER: S-EPMC7744713 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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