↓ Skip to main content

From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality

Overview of attention for article published in Frontiers in Microbiology, November 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

twitter
40 X users

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
157 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality
Published in
Frontiers in Microbiology, November 2017
DOI 10.3389/fmicb.2017.02299
Pubmed ID
Authors

Jan-Ulrich Kreft, Caroline M. Plugge, Clara Prats, Johan H. J. Leveau, Weiwen Zhang, Ferdi L. Hellweger

Abstract

Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 40 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 157 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 26%
Researcher 40 25%
Student > Master 13 8%
Student > Postgraduate 9 6%
Professor > Associate Professor 9 6%
Other 24 15%
Unknown 21 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 26%
Environmental Science 24 15%
Biochemistry, Genetics and Molecular Biology 21 13%
Immunology and Microbiology 8 5%
Earth and Planetary Sciences 8 5%
Other 24 15%
Unknown 31 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 July 2018.
All research outputs
#1,544,756
of 23,340,595 outputs
Outputs from Frontiers in Microbiology
#972
of 25,679 outputs
Outputs of similar age
#36,986
of 440,327 outputs
Outputs of similar age from Frontiers in Microbiology
#31
of 519 outputs
Altmetric has tracked 23,340,595 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,679 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 440,327 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 519 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.