Evidence map›Paper›PMID 26882268›Full record

ArticleThe ISME journal2016

Microbial community modeling using reliability theory.

Julie L Zilles, Luis F Rodríguez, Nicholas A Bartolerio, Angela D Kent

Abstract read
In one paragraph

Article in The ISME journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Julie L ZillesDepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Luis F RodríguezDepartment of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Nicholas A BartolerioDepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Angela D KentDepartment of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.ORCID 0000-0003-1837-2382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Linking microbial community composition with the corresponding ecosystem functions remains challenging. Because microbial communities can differ in their functional responses, this knowledge gap limits ecosystem assessment, design and management. To develop models that explicitly incorporate microbial populations and guide efforts to characterize their functional differences, we propose a novel approach derived from reliability engineering. This reliability modeling approach is illustrated here using a microbial ecology dataset from denitrifying bioreactors. Reliability modeling is well-suited for analyzing the stability of complex networks composed of many microbial populations. It could also be applied to evaluate the redundancy within a particular biochemical pathway in a microbial community. Reliability modeling allows characterization of the system's resilience and identification of failure-prone functional groups or biochemical steps, which can then be targeted for monitoring or enhancement. The reliability engineering approach provides a new perspective for unraveling the interactions between microbial community diversity, functional redundancy and ecosystem services, as well as practical tools for the design and management of engineered ecosystems.

Indexed as

Microbial ConsortiaModels, BiologicalBioreactorsEcosystemReproducibility of Results

Identifiers

PMID26882268
PMCPMC5029169

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.