Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

Volume: 51, Pages: 103 - 108
Published: Jun 1, 2018
Abstract
With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic...
Paper Details
Title
Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries
Published Date
Jun 1, 2018
Volume
51
Pages
103 - 108
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