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迈向人类肠道微生物群的预测模型。

Towards predictive models of the human gut microbiome.

作者信息

Bucci Vanni, Xavier Joao B

机构信息

Department of Biology, University of Massachusetts Dartmouth, 285 Old Westport Road, North Dartmouth, MA 02747, USA.

Program in Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10065, USA.

出版信息

J Mol Biol. 2014 Nov 25;426(23):3907-16. doi: 10.1016/j.jmb.2014.03.017. Epub 2014 Apr 12.

Abstract

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the state of the art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model.

摘要

肠道微生物群是一个易受饮食变化和抗生素治疗等外部干扰影响的生态系统。微生物群落的数学模型在合理设计针对微生物群的饮食和治疗方法方面可能具有巨大价值。在此,我们讨论废水处理生物反应器中微生物群落工程这一其他领域的进展如何能够启发肠道微生物群机械数学模型的开发。我们回顾了生物反应器建模的现状以及当前在肠道微生物群建模方面的努力。数学建模可以从宏基因组研究中涌现的大量数据中大大受益,但诸如网络推断等旨在在没有明确机械知识的情况下预测微生物组动态的数据驱动方法似乎更适合对这些数据进行建模。最后,我们讨论微生物组鸟枪法测序与通量平衡分析等代谢建模方法的整合如何能够实现机械模型的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cdc8/4194272/d7e6ef98d950/nihms585368f1.jpg

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