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• W2892273914 abstract "Abstract Motivation Metabolic networks have evolved to reduce the disruption of key metabolic pathways by the establishment of redundant genes/reactions. Synthetic lethals in metabolic networks provide a window to study these functional redundancies. While synthetic lethals have been previously studied in different organisms, there has been no study on how the synthetic lethals are shaped during adaptation/evolution. Results To understand the adaptive functional redundancies that exist in metabolic networks, we here explore a vast space of ‘random’ metabolic networks evolved on a glucose environment. We examine essential and synthetic lethal reactions in these random metabolic networks, evaluating over 39 billion phenotypes using an efficient algorithm previously developed in our lab, Fast-SL. We establish that nature tends to harbour higher levels of functional redundancies compared with random networks. We then examined the propensity for different reactions to compensate for one another and show that certain key metabolic reactions that are necessary for growth in a particular growth medium show much higher redundancies, and can partner with hundreds of different reactions across the metabolic networks that we studied. We also observe that certain redundancies are unique to environments while some others are observed in all environments. Interestingly, we observe that even very diverse reactions, such as those belonging to distant pathways, show synthetic lethality, illustrating the distributed nature of robustness in metabolism. Our study paves the way for understanding the evolution of redundancy in metabolic networks, and sheds light on the varied compensation mechanisms that serve to enhance robustness. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W2892273914 created "2018-09-27" @default.
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• W2892273914 date "2018-09-01" @default.
• W2892273914 modified "2023-09-27" @default.
• W2892273914 title "Understanding the evolution of functional redundancy in metabolic networks" @default.
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• W2892273914 doi "https://doi.org/10.1093/bioinformatics/bty604" @default.
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