Evidence map›Paper›PMID 33841364›Full record

ArticleFrontiers in microbiology2021

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology.

Lauren M Lui, Erica L-W Majumder, Heidi J Smith, Hans K Carlson, Frederick von Netzer, Matthew W Fields, David A Stahl, Jizhong Zhou, Terry C Hazen, Nitin S Baliga and 2 more

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Co-exposure to polyethylene fiber andApplied and environmental microbiology · 2024
    Article
  5. Article
  6. Article
  7. Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
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

12 authors.

Lauren M LuiDivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
Erica L-W MajumderDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, United States.
Heidi J SmithCenter for Biofilm Engineering, Department of Microbiology and Immunology, Montana State University, Bozeman, MT, United States.
Hans K CarlsonDivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
Frederick von NetzerDepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States.
Matthew W FieldsCenter for Biofilm Engineering, Department of Microbiology and Immunology, Montana State University, Bozeman, MT, United States.
David A StahlDepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States.
Jizhong ZhouInstitute for Environmental Genomics, Department of Microbiology & Plant Biology, School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, United States.
Terry C HazenDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States.
Nitin S BaligaInstitute for Systems Biology, Seattle, WA, United States.
Paul D AdamsDivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
Adam P ArkinDivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Indexed as

metabolic modelreactive transport modelingspecies interaction networksubsurface microbial ecologysystems biology

Identifiers

PMID33841364
PMCPMC8024649

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.