<HashMap><database>biostudies-other</database><scores/><additional><omics_type>Unknown</omics_type><volume>8</volume><submitter>Lucian Smith</submitter><journal>Scientific reports</journal><pagination>2696</pagination><species>Homo sapiens</species><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/MODEL2204290002</full_dataset_link><repository>biostudies-other</repository><additional_accession>10.1038/s41598-018-20348-7</additional_accession><pubmed_authors>Lucian Smith</pubmed_authors><pubmed_authors>Mikal Daou</pubmed_authors></additional><is_claimable>false</is_claimable><name>Jeon2018 - Enzyme clustering in Glucose metabolism</name><description>Miji Jeon, Hye-Won Kang &amp; Songon An. A Mathematical Model for Enzyme Clustering in Glucose Metabolism. Scientific Reports 8, 1 (2018).&lt;p>&lt;/p>&lt;p>&lt;/p>We have recently demonstrated that the rate-limiting enzymes in human glucose metabolism organize into cytoplasmic clusters to form a multienzyme complex, the glucosome, in at least three different sizes. Quantitative high-content imaging data support a hypothesis that the glucosome clusters regulate the direction of glucose flux between energy metabolism and building block biosynthesis in a cluster size-dependent manner. However, direct measurement of their functional contributions to cellular metabolism at subcellular levels has remained challenging. In this work, we develop a mathematical model using a system of ordinary differential equations, in which the association of the rate-limiting enzymes into multienzyme complexes is included as an essential element. We then demonstrate that our mathematical model provides a quantitative principle to simulate glucose flux at both subcellular and population levels in human cancer cells. Lastly, we use the model to simulate 2-deoxyglucose-mediated alteration of glucose flux in a population level based on subcellular high-content imaging data. Collectively, we introduce a new mathematical model for human glucose metabolism, which promotes our understanding of functional roles of differently sized multienzyme complexes in both single-cell and population levels.</description><dates><release>2022-04-29T00:00:00Z</release><modification>2025-07-15T09:43:21.173Z</modification><creation>2025-03-29T22:32:12.749Z</creation></dates><accession>MODEL2204290002</accession><cross_references><sbo>SBO:0000330</sbo><sbo>SBO:0000341</sbo><sbo>SBO:0000394</sbo><sbo>SBO:0000217</sbo><sbo>SBO:0000180</sbo><sbo>SBO:0000526</sbo><sbo>SBO:0000009</sbo><biomodels___db>BIOMD0000001055</biomodels___db><reactome>R-HSA-70475</reactome><reactome>R-HSA-70471</reactome><reactome>R-HSA-71496</reactome><ncit>C28597</ncit><ncit>C16981</ncit><ncit>NCIT:C21034</ncit><chebi>CHEBI:30744</chebi><chebi>CHEBI:78697</chebi><chebi>CHEBI:17234</chebi><chebi>CHEBI:25212</chebi><chebi>CHEBI:15361</chebi><chebi>CHEBI:61304</chebi><chebi>CHEBI:18021</chebi><chebi>CHEBI:64297</chebi><chebi>CHEBI:78682</chebi><mamo>MAMO_0000046</mamo><go>GO:0004634</go><go>GO:0004611</go><go>GO:0006517</go><go>GO:0005737</go><go>GO:0004736</go><go>GO:0008152</go><go>GO:0004619</go><go>GO:0046323</go><go>GO:0016310</go><go>GO:0006006</go><go>GO:0016311</go><taxonomy>9606</taxonomy><uniprot>P30613</uniprot><uniprot>P08237</uniprot><uniprot>P35558</uniprot><uniprot>P09467</uniprot><doi>10.1038/s41598-018-20348-7</doi><unknown>null</unknown></cross_references></HashMap>