{
  "abstract": "Efficient use of limited cellular resources is fundamental to metabolism. Although flux optimization is widely recognized as a central objective of metabolic networks, how flux efficiency influences the allocation of the metabolic proteome remains unclear and lacks direct validation. Here, we derive a simple analytical relationship linking the equilibrium constant (K) and the catalytic-abundance quotient (CAQ) of reactions within a pathway that defines the condition for maximal efficiency. By integrating reaction thermodynamics, enzyme kinetics, transcriptomic, and proteomic data, we compared enzyme allocation in glycolysis and the TCA cycle with this K-CAQ relationship across evolutionarily distant species, and showed that deviation from it can be explained by a trade-off between maximizing flux efficiency and concentrating flux control. Moreover, the drive to optimize glycolytic efficiency is strengthened under oncogenic signaling and a limited cellular budget for glycolytic enzymes. These findings establish a principle of Pareto optimality governing enzyme allocation in metabolic pathways and reveal key determinants of efficiency optimality in glycolysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Kai Sun"
    },
    {
      "name": "Weiyan Zheng"
    },
    {
      "name": "Wenchao Fan",
      "orcid": "0009-0004-4238-9816"
    },
    {
      "name": "Chuyun Ding"
    },
    {
      "name": "Dan Huang"
    },
    {
      "name": "Ziwei Dai",
      "orcid": "0000-0002-0858-638X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/82wamnp8fferd.bib",
  "cited_by": 0,
  "doi": "10.1038/s44320-026-00250-5",
  "fulltext_url": null,
  "id": "82wamnp8fferd",
  "issns": [
    "1744-4292"
  ],
  "landing_url": "https://link.springer.com/10.1038/s44320-026-00250-5",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Microbial Metabolic Engineering and Bioproduction"
    ]
  ],
  "openalex": "W7218513531",
  "pdf_url": "https://link.springer.com/content/pdf/10.1038/s44320-026-00250-5.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/82wamnp8fferd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:37Z",
      "native_id": "10.1038/s44320-026-00250-5",
      "source": "crossref",
      "url": "https://link.springer.com/10.1038/s44320-026-00250-5"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Microbial Metabolic Engineering and Bioproduction",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Trade-off between flux efficiency and metabolic control shapes enzyme allocation",
  "title_text": "Trade-off between flux efficiency and metabolic control shapes enzyme allocation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:37Z",
  "url": "https://research.draconitic.com/paper/82wamnp8fferd",
  "venue": "Molecular Systems Biology",
  "versions": []
}