Evidence map›Paper›PMID 24992662›Full record

ArticlePLoS computational biology2014

Emergent biosynthetic capacity in simple microbial communities.

Hsuan-Chao Chiu, Roie Levy, Elhanan Borenstein

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing 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

54 citing papers in PubMed.

  1. Article
  2. Article
  3. COmmunity and Single Microbe Optimisation System (COSMOS).NPJ systems biology and applications · 2025
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Metabolic modelling approaches for describing and engineering microbial communities.Computational and structural biotechnology journal · 2021
    Review
  13. Article
  14. Review
  15. Observational
  16. Review
  17. Article
  18. μBialSim: Constraint-Based Dynamic Simulation of Complex Microbiomes.Frontiers in bioengineering and biotechnology · 2020
    Article
  19. Article
  20. 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

3 authors.

Hsuan-Chao ChiuDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Roie LevyDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Elhanan BorensteinDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America; Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America; Santa Fe Institute, Santa Fe, New Mexico, United States of America.

Funding

A Computational Framework for Designing Microbiome ManipulationDP2AT007802 · NCCIH · UNIVERSITY OF WASHINGTON · PI BORENSTEIN, ELHANAN · 2012 to 2012
$2.3M
NCCIH NIH HHS DP2 AT007802NCCIH NIH HHS DP2 AT 007802-01
6 · The paper itself

Abstract

Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity. Here we present a comprehensive computational framework, integrating high-quality metabolic models of multiple species, temporal dynamics, and flux variability analysis, to study the metabolic capacity and dynamics of simple two-species microbial ecosystems. We specifically focus on detecting emergent biosynthetic capacity--instances in which a community growing on some medium produces and secretes metabolites that are not secreted by any member species when growing in isolation on that same medium. Using this framework to model a large collection of two-species communities on multiple media, we demonstrate that emergent biosynthetic capacity is highly prevalent. We identify commonly observed emergent metabolites and metabolic reprogramming patterns, characterizing typical mechanisms of emergent capacity. We further find that emergent secretion tends to occur in two waves, the first as soon as the two organisms are introduced, and the second when the medium is depleted and nutrients become limited. Finally, aiming to identify global community determinants of emergent capacity, we find a marked association between the level of emergent biosynthetic capacity and the functional/phylogenetic distance between community members. Specifically, we demonstrate a "Goldilocks" principle, where high levels of emergent capacity are observed when the species comprising the community are functionally neither too close, nor too distant. Taken together, our results demonstrate the potential to design and engineer synthetic communities capable of novel metabolic activities and point to promising future directions in environmental and clinical bioengineering.

Indexed as

EcosystemModels, BiologicalComputational BiologyMetabolismMicrobial Consortia

Identifiers

PMID24992662
PMCPMC4084645

What OpenQuestion holds

Textmetadata
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.