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Cited by 324

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Engineering Escherichia coli coculture systems for the production of biochemical products

Open access
Zhang, Haoran, Pereira, Brian, Li, Zhengjun, Stephanopoulos, Gregory·2015·Proceedings of the National Academy of Sciences

Abstract

Significance: Production of industrial compounds by using engineered microorganisms is a robust method to reduce our reliance on nonrenewable petroleum resources and increase the utility of renewable resources, such as lignocellulose. Because there are major limitations for engineering a single microbial cell to achieve high-yield production, we developed microbial coculture systems consisting of two different microbial cell types to use sugar mixtures that can be derived from lignocellulose efficiently. We demonstrate that this approach is successful for achieving high-level production of two important value-added molecules, cis , cis -muconic acid and 4-hydroxybenzoic acid. This accomplishment establishes a previously unidentified technology for advancing future research in metabolic engineering and synthetic biology.

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Crossref · 10.1073/pnas.1506781112

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Metadata

Authors
Zhang, H., Pereira, B., Li, Z., & Stephanopoulos, G.
Venue
Proceedings of the National Academy of Sciences
Published
2015-06-25
Volume
112
Issue
27
Pages
8266-8271
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1958250075
Cited by
324
References
30
FWCI
10.68
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Engineering Escherichia coli coculture systems for the production of biochemical products. https://app.cochat.ai/papers/10.1073/pnas.1506781112
The paper
Zhang, H., Pereira, B., Li, Z., & Stephanopoulos, G. (2015). Engineering Escherichia coli coculture systems for the production of biochemical products. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1506781112

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.