Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries. (June 2018) - Pan, Shu; Reed, Jennifer L
Graphical abstract: Highlights: Gap-filling is an important tool for making model-driven metabolic discoveries. Recent gap-filling algorithms are more efficient and/or show superior predictions. Advances in experimental techniques lead to faster and more accurate gap-filling. 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 models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.