Metagenomic systems biology and metabolic modeling of the human microbiome

@article{Levy2014MetagenomicSB,
  title={Metagenomic systems biology and metabolic modeling of the human microbiome},
  author={Roie Levy and Elhanan Borenstein},
  journal={Gut Microbes},
  year={2014},
  volume={5},
  pages={265 - 270 - 9}
}
The human microbiome is a key contributor to health and development. Yet little is known about the ecological forces that are at play in defining the composition of such host-associated communities. Metagenomics-based studies have uncovered clear patterns of community structure but are often incapable of distinguishing alternative structuring paradigms. In a recent study, we integrated metagenomic analysis with a systems biology approach, using a reverse ecology framework to model numerous… 
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38 Citations
Background Citations
23
Methods Citations
1

38 Citations

Intestinal microbiome landscaping: insight in community assemblage and implications for microbial modulation strategies
TLDR
Key concepts of the human intestinal microbiome landscape, i.e. the compositional and functional ‘core’, the presence of community types and the existence of alternative stable states are discussed, as well as core taxa revealed functional redundancy, which is expected to stabilize the ecosystem.
Mapping the inner workings of the microbiome: genomic- and metagenomic-based study of metabolism and metabolic interactions in the human microbiome.
Computational Modeling of the Human Microbiome
TLDR
By providing an overview of different human microbiome sites, this work hopes to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
Fitness effects of host-bacterial interactions – the microbial perspective
TLDR
It is found that rare microbiome members are essential for achieving the observed community composition, which ultimately sets the maximum carrying capacity.
Microbial Species Coexistence Depends on the Host Environment
TLDR
Both microbial species can only stably coexist in the host environment, whereas Duganella outcompetes Curvibacter in both in vitro environments irrespective of initial starting frequencies, which indicates that rare microbial community members might be relevant for achieving the native community composition and carrying capacity.
RevEcoR: an R package for the reverse ecology analysis of microbiomes
TLDR
RevEcoR, an R package and a Shiny Web application that implements the reverse ecology algorithm for determining microbe–microbe interactions in microbial communities allows users to obtain large-scale ecological insights into species’ ecology directly from high-throughput metagenomic data.
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics
TLDR
The two main approaches for analyzing the microbiome, 16S ribosomal RNA gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses and several methods for taxonomic classification of bacterial sequences are discussed.
Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome
TLDR
This work demonstrates that the functional uniformity observed in microbiome studies can be attributed, at least partly, to common computational metagenomic processing procedures that mask true functional variation across microbiome samples, and uncovers such hidden functional variation.
Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome
Significance Deep molecular phenotyping of individuals provides the opportunity for biological insight into host physiology. As the human microbiome is increasingly being recognized as an important…
Mapping the InnerWorkings of theMicrobiome : Genomic-andMetagenomic-BasedStudyofMetabolism andMetabolic Interactions in the HumanMicrobiome
TLDR
Two interrelated lines of work are highlighted, the first aiming to deconvolve the microbiome and to characterize the metabolic capacity of various microbiome species and the second aiming to utilize computational modeling to infer and study metabolic interactions between these species.
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