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Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions
Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How response to frequent perturbations in in...
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Published in: | Metabolites 2023-01, Vol.13 (1), p.88 |
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creator | Lao-Martil, David Verhagen, Koen J A Valdeira Caetano, Ana H Pardijs, Ilse H van Riel, Natal A W Wahl, S Aljoscha |
description | Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How
response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome,
C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles. |
doi_str_mv | 10.3390/metabo13010088 |
format | article |
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C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.</description><identifier>ISSN: 2218-1989</identifier><identifier>EISSN: 2218-1989</identifier><identifier>DOI: 10.3390/metabo13010088</identifier><identifier>PMID: 36677014</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adaptation ; Bioreactors ; Carbohydrate metabolism ; Carbohydrates ; Carbon ; carbon storage metabolism ; Environmental conditions ; Enzyme kinetics ; Ethanol ; Experiments ; Glucose ; glucose transport ; Glycerol ; Hexose ; Intracellular ; kinetic modeling ; Metabolic flux ; Metabolic response ; Metabolism ; Metabolites ; Phosphorylation ; Proteomes ; Proteomics ; repeated substrate perturbation regime ; Saccharomyces cerevisiae ; Yeast</subject><ispartof>Metabolites, 2023-01, Vol.13 (1), p.88</ispartof><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 by the authors. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c484t-ac1efe43b10358b65c600163404ff731f679db0ecbe4421863a8975f66c315cf3</citedby><cites>FETCH-LOGICAL-c484t-ac1efe43b10358b65c600163404ff731f679db0ecbe4421863a8975f66c315cf3</cites><orcidid>0000-0001-7232-4352 ; 0000-0002-1615-3875 ; 0000-0001-9375-4730</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2767238554/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2767238554?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36677014$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lao-Martil, David</creatorcontrib><creatorcontrib>Verhagen, Koen J A</creatorcontrib><creatorcontrib>Valdeira Caetano, Ana H</creatorcontrib><creatorcontrib>Pardijs, Ilse H</creatorcontrib><creatorcontrib>van Riel, Natal A W</creatorcontrib><creatorcontrib>Wahl, S Aljoscha</creatorcontrib><title>Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions</title><title>Metabolites</title><addtitle>Metabolites</addtitle><description>Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How
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C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.</description><subject>Adaptation</subject><subject>Bioreactors</subject><subject>Carbohydrate metabolism</subject><subject>Carbohydrates</subject><subject>Carbon</subject><subject>carbon storage metabolism</subject><subject>Environmental conditions</subject><subject>Enzyme kinetics</subject><subject>Ethanol</subject><subject>Experiments</subject><subject>Glucose</subject><subject>glucose transport</subject><subject>Glycerol</subject><subject>Hexose</subject><subject>Intracellular</subject><subject>kinetic modeling</subject><subject>Metabolic flux</subject><subject>Metabolic response</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Phosphorylation</subject><subject>Proteomes</subject><subject>Proteomics</subject><subject>repeated substrate perturbation regime</subject><subject>Saccharomyces cerevisiae</subject><subject>Yeast</subject><issn>2218-1989</issn><issn>2218-1989</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkk1vGyEQhldVqyZKc-2xWqmXXpzCwsJyqRS5X1YT9eDmjAZ2sLF2wYV1JP-D_uxiO42ScmE0vDzMvExVvaXkijFFPo44gYmUEUpI172ozpuGdjOqOvXySXxWXea8IWUJ0kpCX1dnTAhZIn5e_bnLPqzqHz7g5G19G3schkNmivUiOEz1dQ_bCSYfQ659qJdg7RpSHPcWc20x4b3PHrCeQzJxve8TTFgvp5hghfXtscLB5_EA_LwPMJZXljuTp6NuHkPvj-w31SsHQ8bLh_2iuvv65df8--zm57fF_PpmZnnHpxlYig45M5SwtjOitYIQKhgn3DnJqBNS9YagNch5cUAw6JRsnRCW0dY6dlEtTtw-wkZvkx8h7XUEr4-JmFYaUrFiQM1ANEZQ6YwRXCippJXOKkXbBgjIprA-nVjbnRmxtxhKV8Mz6POT4Nd6Fe-16kRDFSuADw-AFH_vME969NmWH4CAcZd1I0XXcMEaWqTv_5Nu4i6FYtVBVYrp2pYX1dVJZVPMOaF7LIYSfZgZ_XxmyoV3T1t4lP-bEPYXIli_mA</recordid><startdate>20230105</startdate><enddate>20230105</enddate><creator>Lao-Martil, David</creator><creator>Verhagen, Koen J A</creator><creator>Valdeira Caetano, Ana H</creator><creator>Pardijs, Ilse H</creator><creator>van Riel, Natal A W</creator><creator>Wahl, S Aljoscha</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7232-4352</orcidid><orcidid>https://orcid.org/0000-0002-1615-3875</orcidid><orcidid>https://orcid.org/0000-0001-9375-4730</orcidid></search><sort><creationdate>20230105</creationdate><title>Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions</title><author>Lao-Martil, David ; Verhagen, Koen J A ; Valdeira Caetano, Ana H ; Pardijs, Ilse H ; van Riel, Natal A W ; Wahl, S Aljoscha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c484t-ac1efe43b10358b65c600163404ff731f679db0ecbe4421863a8975f66c315cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptation</topic><topic>Bioreactors</topic><topic>Carbohydrate metabolism</topic><topic>Carbohydrates</topic><topic>Carbon</topic><topic>carbon storage metabolism</topic><topic>Environmental conditions</topic><topic>Enzyme kinetics</topic><topic>Ethanol</topic><topic>Experiments</topic><topic>Glucose</topic><topic>glucose transport</topic><topic>Glycerol</topic><topic>Hexose</topic><topic>Intracellular</topic><topic>kinetic modeling</topic><topic>Metabolic flux</topic><topic>Metabolic response</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Phosphorylation</topic><topic>Proteomes</topic><topic>Proteomics</topic><topic>repeated substrate perturbation regime</topic><topic>Saccharomyces cerevisiae</topic><topic>Yeast</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lao-Martil, David</creatorcontrib><creatorcontrib>Verhagen, Koen J A</creatorcontrib><creatorcontrib>Valdeira Caetano, Ana H</creatorcontrib><creatorcontrib>Pardijs, Ilse H</creatorcontrib><creatorcontrib>van Riel, Natal A W</creatorcontrib><creatorcontrib>Wahl, S Aljoscha</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ProQuest Biological Science Journals</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest - Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Metabolites</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lao-Martil, David</au><au>Verhagen, Koen J A</au><au>Valdeira Caetano, Ana H</au><au>Pardijs, Ilse H</au><au>van Riel, Natal A W</au><au>Wahl, S Aljoscha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions</atitle><jtitle>Metabolites</jtitle><addtitle>Metabolites</addtitle><date>2023-01-05</date><risdate>2023</risdate><volume>13</volume><issue>1</issue><spage>88</spage><pages>88-</pages><issn>2218-1989</issn><eissn>2218-1989</eissn><abstract>Microbial metabolism is strongly dependent on the environmental conditions. 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C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>36677014</pmid><doi>10.3390/metabo13010088</doi><orcidid>https://orcid.org/0000-0001-7232-4352</orcidid><orcidid>https://orcid.org/0000-0002-1615-3875</orcidid><orcidid>https://orcid.org/0000-0001-9375-4730</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptation Bioreactors Carbohydrate metabolism Carbohydrates Carbon carbon storage metabolism Environmental conditions Enzyme kinetics Ethanol Experiments Glucose glucose transport Glycerol Hexose Intracellular kinetic modeling Metabolic flux Metabolic response Metabolism Metabolites Phosphorylation Proteomes Proteomics repeated substrate perturbation regime Saccharomyces cerevisiae Yeast |
title | Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions |
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