TY - JOUR
T1 - A hybrid flux balance analysis and machine learning pipeline elucidates the metabolic response of cyanobacteria to different growth conditions
AU - Vijayakumar, Supreeta
AU - Rahman, PattanathuKaja-Mohideen Sheikh Mujibur
AU - Angione, Claudio
PY - 2020/11/18
Y1 - 2020/11/18
N2 - 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 (FBA) to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal component analysis (PCA), 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.
AB - 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 (FBA) to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal component analysis (PCA), 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.
U2 - 10.1016/j.isci.2020.101818
DO - 10.1016/j.isci.2020.101818
M3 - Article
SN - 2589-0042
JO - iScience
JF - iScience
M1 - 101818
ER -