Evidence map›Paper›PMID 28716030›Full record

ArticleBMC systems biology2017

Modeling de novo granulation of anaerobic sludge.

Anna Doloman, Honey Varghese, Charles D Miller, Nicholas S Flann

Abstract read
In one paragraph

Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Mechanisms of microbial co-aggregation in mixed anaerobic cultures.Applied microbiology and biotechnology · 2024
    Review
  2. Review
  3. Review
  4. Article
  5. Multiscale Modelling of De Novo Anaerobic Granulation.Bulletin of mathematical biology · 2021
    Article
  6. Review
  7. Review
  8. A Model for Bioaugmented Anaerobic Granulation.Frontiers in microbiology · 2020
    Article
  9. Article
  10. Article
  11. 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

4 authors.

Anna DolomanDepartment of Biological Engineering, Utah State University, Old Main Hill 4105, Logan, 84322-4105, UT, USA.
Honey VargheseDepartment of Computer Science, Utah State University, Old Main Hill 420, Logan, 84322-4205, UT, USA.
Charles D MillerDepartment of Biological Engineering, Utah State University, Old Main Hill 4105, Logan, 84322-4105, UT, USA.
Nicholas S FlannDepartment of Computer Science, Utah State University, Old Main Hill 420, Logan, 84322-4205, UT, USA. nick.flann@usu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA unique combination of mechanical, physiochemical and biological forces influences granulation during processes of anaerobic digestion. Understanding this process requires a systems biology approach due to the need to consider not just single-cell metabolic processes, but also the multicellular organization and development of the granule.

resultsIn this computational experiment, we address the role that physiochemical and biological processes play in granulation and provide a literature-validated working model of anaerobic granule de novo formation. The agent-based model developed in a cDynoMiCs simulation environment successfully demonstrated a de novo granulation in a glucose fed system, with the average specific methanogenic activity of 1.11 ml C H

conclusionsIn the model, the emergence of multicellular organization of anaerobic granules from randomly mixed population of methanogens and acidogens was observed and validated. The model of anaerobic de novo granulation can be used to predict the morphology of the anaerobic granules in a alternative substrates of interest and to estimate methane potential of the resulting microbial consortia. The study demonstrates a successful integration of a systems biology approach to model multicellular systems with the engineering of an efficient anaerobic digestion system.

Indexed as

Models, BiologicalAnaerobiosisBiomechanical PhenomenaBioreactorsSewageSystems BiologySewageAgent-based modelingAnaerobic granulationMulticellular modelingWastewater treatment

Identifiers

PMID28716030
PMCPMC5514506

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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.