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Cited by 295

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Metabolic division of labor in microbial systems

Open access
Tsoi, Ryan, Wu, Feilun, Zhang, Carolyn, Bewick, Sharon, Karig, David, You, Lingchong·2018·Proceedings of the National Academy of Sciences

Abstract

Significance: If contained in a single population a complex metabolic pathway can impose a burden on the host, decreasing the system’s overall productivity. This limitation can be overcome by division of labor (DOL), where distinct populations perform different steps of the pathway, thus reducing the burden on each population. By compartmentalizing reactions, however, DOL reduces their efficiency by introducing a transport barrier for metabolites and enzymes. It remains unclear how the trade-off between reducing burden and decreasing reaction efficiency dictates the potential benefit of DOL. Through the analysis of different metabolic pathways we derive a general criterion establishing when DOL outperforms a single population. Our results can guide rational engineering of metabolic pathways and provide insights into operation of natural pathways.

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Metadata

Authors
Tsoi, R., Wu, F., Zhang, C., Bewick, S., Karig, D., & You, L.
Venue
Proceedings of the National Academy of Sciences
Published
2018-02-20
Volume
115
Issue
10
Pages
2526-2531
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2793358513
Cited by
295
References
6
FWCI
6.51
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Metabolic division of labor in microbial systems. https://app.cochat.ai/papers/10.1073/pnas.1716888115
The paper
Tsoi, R., Wu, F., Zhang, C., Bewick, S., Karig, D., & You, L. (2018). Metabolic division of labor in microbial systems. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1716888115

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.