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- Ji, Boyang, 1983, et al.
(författare)
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From next-generation sequencing to systematic modeling of the gut microbiome
- 2015
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Ingår i: Frontiers in Genetics. - : Frontiers Media SA. - 1664-8021. ; 6:JUN
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Forskningsöversikt (refereegranskat)abstract
- Changes in the human gut microbiome are associated with altered human metabolism and health, yet the mechanisms of interactions between microbial species and human metabolism have not been clearly elucidated. Next-generation sequencing has revolutionized the human gut microbiome research, but most current applications concentrate on studying the microbial diversity of communities and have at best provided associations between specific gut bacteria and human health. However, little is known about the inner metabolic mechanisms in the gut ecosystem. Here we review recent progress in modeling the metabolic interactions of gut microbiome, with special focus on the utilization of metabolic modeling to infer host-microbe interactions and microbial species interactions. The systematic modeling of metabolic interactions could provide a predictive understanding of gut microbiome, and pave the way to synthetic microbiota design and personalized-microbiome medicine and healthcare. Finally, we discuss the integration of metabolic modeling and gut microbiome engineering, which offer a new way to explore metabolic interactions across members of the gut microbiota.
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| 2. |
- Ji, Boyang, 1983, et al.
(författare)
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New insight into the gut microbiome through metagenomics
- 2015
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Ingår i: Advances in Genomics and Genetics. - 1179-9870. ; 5, s. 77-91
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Tidskriftsartikel (refereegranskat)abstract
- The human gut is colonized by different types of microorganisms, which are known to play important roles in the human host by maintaining physiological homeostasis. The human host provides a nutrient-rich environment, and the microbiota provides some necessary functions that humans cannot perform. A comprehensive analysis of the human gut microbiome is thus important for revealing the mechanisms of these host–microbe interactions. The development of high-throughput sequencing technology and related computational frameworks enables exploration of the metabolic interactions and their roles in human health and diseases. Herein, we describe the metagenomic methods used in human gut microbiome studies and review the roles of gut microbiota as well as the integrative analyses of metagenomic data with other omics data. Finally, we discuss the application of constraint-based modeling to elucidate the microbe–microbe interaction and host–microbe interaction in the human gut microbiota.
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| 3. |
- Zhang, C., et al.
(författare)
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Logical transformation of genome-scale metabolic models for gene level applications and analysis
- 2015
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 31:14, s. 2324-2331
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of genereaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites. Results: Here, we propose a framework named logic transformation of model (LTM) that is able to simplify the gene-reaction associations and enables integration with other developed methods for gene level applications. We show that the transformed GEMs have increased reaction and metabolite number as well as degree of freedom in flux balance analysis, but the gene-reaction associations and the main features of flux distributions remain constant. In addition, we develop two methods, OptGeneKnock and FastGeneSL by combining LTM with previously developed reaction-based methods. We show that the FastGeneSL outperforms exhaustive search. Finally, we demonstrate the use of the developed methods in two different case studies. We could design fast genetic intervention strategies for targeted overproduction of biochemicals and identify double and triple synthetic lethal gene sets for inhibition of hepatocellular carcinoma tumor growth through the use of OptGeneKnock and FastGeneSL, respectively.
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