Abstract
Synechococcus sp. PCC 7002 is a fast-growing cyanobacterium which flourishes in freshwater and marine environments, owing to its ability to tolerate high light intensity and a wide range of salinities. Harnessing the properties of cyanobacteria and understanding their metabolic efficiency has become an imperative goal in recent years owing to their potential to serve as biocatalysts for the production of renewable biofuels. To improve characterisation of metabolic networks, genome-scale models of metabolism can be integrated with multi-omic data to provide a more accurate representation of metabolic capability and refine phenotypic predictions. In this work, a heuristic pipeline is constructed for analysing a genome-scale metabolic model of Synechococcus sp. PCC 7002, which utilises flux balance analysis across multiple layers to observe flux response between conditions across four key pathways. Across various conditions, the detection of significant patterns and mechanisms to cope with fluctuations in light intensity and salinity provides insights into the maintenance of metabolic efficiency.
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Vijayakumar, S., Angione, C. (2017). Multi-omic Data Integration Elucidates Synechococcus Adaptation Mechanisms to Fluctuations in Light Intensity and Salinity. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_19
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DOI: https://doi.org/10.1007/978-3-319-56148-6_19
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