A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of the Saccharomyces Genus in Fermentation
Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substant...
Saved in:
Published in: | mSystems Vol. 6; no. 4; p. e0026021 |
---|---|
Main Authors: | , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
American Society for Microbiology
31-08-2021
|
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Abstract | Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts' metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories.
Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the
genus in rich medium fermentation. The study revealed that cryotolerant
species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. |
---|---|
AbstractList | Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation.
Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories.
IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts' metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the genus in rich medium fermentation. The study revealed that cryotolerant species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories. |
Author | Barrio, Eladio Querol, Amparo Mendoza, Sebastián N Teusink, Bas Minebois, Romain Pérez-Torrado, Roberto Henriques, David Balsa-Canto, Eva Macías, Laura G |
Author_xml | – sequence: 1 givenname: David surname: Henriques fullname: Henriques, David organization: (Bio)process Engineering Group, IIM-CSIC, Vigo, Spain – sequence: 2 givenname: Romain surname: Minebois fullname: Minebois, Romain organization: Systems Biology in Yeast of Biotechnological Interest, IATA-CSIC, Paterna, Spain – sequence: 3 givenname: Sebastián N surname: Mendoza fullname: Mendoza, Sebastián N organization: Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands – sequence: 4 givenname: Laura G surname: Macías fullname: Macías, Laura G organization: Systems Biology in Yeast of Biotechnological Interest, IATA-CSIC, Paterna, Spain – sequence: 5 givenname: Roberto surname: Pérez-Torrado fullname: Pérez-Torrado, Roberto organization: Systems Biology in Yeast of Biotechnological Interest, IATA-CSIC, Paterna, Spain – sequence: 6 givenname: Eladio surname: Barrio fullname: Barrio, Eladio organization: Department of Genetics, University of Valencia, Burjassot, Spain – sequence: 7 givenname: Bas surname: Teusink fullname: Teusink, Bas organization: Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands – sequence: 8 givenname: Amparo surname: Querol fullname: Querol, Amparo organization: Systems Biology in Yeast of Biotechnological Interest, IATA-CSIC, Paterna, Spain – sequence: 9 givenname: Eva surname: Balsa-Canto fullname: Balsa-Canto, Eva organization: (Bio)process Engineering Group, IIM-CSIC, Vigo, Spain |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34342535$$D View this record in MEDLINE/PubMed |
BookMark | eNqNks1u1DAQxyNUREvpA3BBPnJJ8UccJxek0tJSqQiJwIGT5djjXa_ieLGTwj4I74vblKoVEuJij8Yzv_HM_J8Xe2MYoSheEnxMCG3e-G6XJvDpGGNa45KSJ8UBZaItORZi74G9XxyltMEYk5oJQttnxT6rWEU54wfFrxP0cR4mt12rBIsZ-g3oyV0DOtuNyjuNLmAMHspOqyHHBAMD6tbhR0JnzlqIME7oM5jwE71Tgxq1G1dI-ZDPb6DShLotaAcJBYumNaBOab1WMfidzs7MnhNyIzqH6DNJ5Q-ML4qnVg0Jju7uw-Lr-fsvpx_Kq08Xl6cnV6XiVT2VtRaWc1OBqmssKmFw1ZgW99TUIDhwxniFqW6sUTr33rR9L2oCNTBbt9oQdlhcLlwT1EZuo_Mq7mRQTt46QlxJFSenB5CsZg3tte2xVVXT2MaQ1hpCWqxFT0iTWW8X1nbuPRide4lqeAR9_DK6tVyFa9lUWDBaZcDrO0AM32dIk_QuaRjySCHMSVLOBc97EzehZAnVMaQUwd6XIVjeqEP6tKhD3qpDUvI_Od3fOcdLjkqeyk2Y45jX8c-EVw-HcF_ij-DYb5VG3DY |
CitedBy_id | crossref_primary_10_1007_s11705_024_2410_8 crossref_primary_10_1002_bit_28421 crossref_primary_10_1186_s12859_023_05506_7 crossref_primary_10_1093_jimb_kuac025 crossref_primary_10_1111_1751_7915_14211 crossref_primary_10_1016_j_lwt_2023_115705 crossref_primary_10_1051_bioconf_20236802040 crossref_primary_10_1016_j_biotechadv_2024_108319 crossref_primary_10_1093_gbe_evad207 crossref_primary_10_3390_foods11121682 crossref_primary_10_1093_femsyr_foac003 crossref_primary_10_1016_j_synbio_2021_12_010 crossref_primary_10_1016_j_foodres_2022_112016 crossref_primary_10_1007_s00253_022_12066_y crossref_primary_10_1128_spectrum_03519_22 crossref_primary_10_1186_s13068_022_02113_1 crossref_primary_10_1016_j_ijfoodmicro_2023_110537 crossref_primary_10_1016_j_fm_2022_103981 |
Cites_doi | 10.1038/msb.2009.77 10.1038/s41467-019-12961-5 10.1093/nar/gkq1268 10.3390/fermentation4030054 10.1186/s12918-017-0408-2 10.1007/BF00058655 10.1111/1462-2920.13617 10.3389/fmicb.2017.00150 10.1146/annurev-micro-091213-113025 10.1186/1752-0509-4-11 10.1099/mic.0.26007-0 10.1038/msb.2010.47 10.1111/1462-2920.15135 10.1002/bit.27370 10.1371/journal.pone.0060135 10.3389/fgene.2019.00082 10.1111/j.1365-2672.2009.04196.x 10.1016/s0168-1605(01)00552-9 10.1093/bioinformatics/btw411 10.1016/j.ymben.2021.02.004 10.1093/femsre/fuaa058 10.1128/AEM.02617-16 10.1091/mbc.e03-11-0856 10.1002/yea.2962 10.1038/s41467-019-11581-3 10.20870/oeno-one.2020.54.2.2594 10.1186/1752-0509-5-75 10.1186/s12934-018-1018-4 10.1007/s10479-006-0086-8 10.1128/aem.58.9.2948-2953.1992 10.1007/s00253-019-09664-8 10.1371/journal.pcbi.1003580 10.1007/s11306-015-0819-2 10.1016/j.copbio.2020.01.008 10.1016/j.biotechadv.2019.02.005 10.1128/AEM.70.6.3392-3400.2004 10.1007/s00253-016-7970-1 10.1099/13500872-142-8-2299 10.1126/science.274.5287.546 10.1002/yea.1046 10.1093/femsyr/fox050 10.1186/s12934-014-0109-0 10.1111/jam.12126 10.1016/j.gde.2015.10.008 10.1016/j.cell.2016.02.004 10.1038/nprot.2011.308 10.1016/j.fm.2019.103287 10.1002/bit.21332 10.1007/s10898-007-9172-y 10.1002/bit.10133 10.15252/msb.20167411 10.1128/AEM.02294-12 10.1186/1475-2859-13-85 10.1074/jbc.M007103200 10.1038/nbt.1614 10.1016/j.ymben.2014.07.004 10.1128/aem.60.10.3724-3731.1994 10.1016/j.ijfoodmicro.2017.04.002 10.1111/1751-7915.12488 10.1128/AEM.01029-07 10.3390/pr8091195 10.1016/j.copbio.2017.03.017 10.1016/j.fm.2020.103484 10.1002/bit.21494 10.1016/j.micres.2016.07.010 10.5344/ajev.2009.60.4.508 10.1016/B978-0-12-815271-3.00013-0 10.1007/978-3-642-21551-3_33 |
ContentType | Journal Article |
Copyright | Copyright © 2021 Henriques et al. Copyright © 2021 Henriques et al. 2021 Henriques et al. |
Copyright_xml | – notice: Copyright © 2021 Henriques et al. – notice: Copyright © 2021 Henriques et al. 2021 Henriques et al. |
DBID | NPM AAYXX CITATION 7X8 5PM DOA |
DOI | 10.1128/mSystems.00260-21 |
DatabaseName | PubMed CrossRef MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef MEDLINE - Academic |
DatabaseTitleList | CrossRef CrossRef PubMed |
Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: http://www.doaj.org/ sourceTypes: Open Website |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Biology |
EISSN | 2379-5077 |
Editor | Del Vecchio, Domitilla |
Editor_xml | – sequence: 1 givenname: Domitilla surname: Del Vecchio fullname: Del Vecchio, Domitilla |
EndPage | e0026021 |
ExternalDocumentID | oai_doaj_org_article_36382bcfb0fa488f8d19fd1190c7b118 10_1128_msystems_00260_21 10_1128_mSystems_00260_21 mSystems00260-21 34342535 |
Genre | Journal Article |
GrantInformation_xml | – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: RTI2018-093744-B-C31 – fundername: CONICYTBecasChile grantid: 72180373 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: PID2019-104113RB-I00 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: BES-2016-07820 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: RTI2018-093744-B-C32 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: RTI2018-093744-B-C33 – fundername: EraCoBioTech grantid: 053.80.733 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: PID2019-104113RB-I00 funderid: https://doi.org/10.13039/100014440 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: BES-2016-07820 funderid: https://doi.org/10.13039/100014440 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: RTI2018-093744-B-C33 funderid: https://doi.org/10.13039/100014440 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: RTI2018-093744-B-C32 funderid: https://doi.org/10.13039/100014440 – fundername: Ministerio de Ciencia, Innovación y Universidades (MCIU) grantid: RTI2018-093744-B-C31 funderid: https://doi.org/10.13039/100014440 – fundername: ; grantid: PID2019-104113RB-I00 – fundername: ; grantid: 053.80.733 – fundername: ; grantid: RTI2018-093744-B-C32 – fundername: ; grantid: BES-2016-07820 – fundername: ; grantid: RTI2018-093744-B-C31 – fundername: ; grantid: 72180373 – fundername: ; grantid: RTI2018-093744-B-C33 |
GroupedDBID | 0R~ 3V. 53G 5VS 7X7 8FE 8FH 8FI 8FJ AAFWJ AAUOK ABUWG ACPRK ADBBV AFKRA AFPKN AHMBA ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI CCPQU EBS FRP FYUFA GROUPED_DOAJ H13 HCIFZ HMCUK HYE KQ8 LK8 M48 M7P M~E NPM O9- OK1 PIMPY PQQKQ PROAC RHI RPM RSF UKHRP 0R ADACO BBAFP BXI PQEST PQUKI PRINS AAYXX CITATION 7X8 5PM |
ID | FETCH-LOGICAL-a546t-6c7f55d4ea660747d048d90b2d6e75e5335402c8fdac63789bb761e6e3f69cd13 |
IEDL.DBID | RPM |
ISSN | 2379-5077 |
IngestDate | Tue Oct 22 15:10:05 EDT 2024 Tue Sep 17 21:14:16 EDT 2024 Fri Oct 25 07:04:45 EDT 2024 Fri Nov 22 03:04:32 EST 2024 Thu Nov 21 22:38:32 EST 2024 Tue Dec 28 13:57:58 EST 2021 Sat Sep 28 08:27:28 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 4 |
Keywords | dynamic genome-scale models redox balance Saccharomyces species batch fermentation cryotolerant species |
Language | English |
License | This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. https://creativecommons.org/licenses/by/4.0 This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-a546t-6c7f55d4ea660747d048d90b2d6e75e5335402c8fdac63789bb761e6e3f69cd13 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Citation Henriques D, Minebois R, Mendoza SN, Macías LG, Pérez-Torrado R, Barrio E, Teusink B, Querol A, Balsa-Canto E. 2021. A multiphase multiobjective dynamic genome-scale model shows different redox balancing among yeast species of the Saccharomyces genus in fermentation. mSystems 6:e00260-21. https://doi.org/10.1128/mSystems.00260-21. |
ORCID | 0000-0002-6478-6845 0000-0003-3929-0423 0000-0002-9477-292X |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407324/ |
PMID | 34342535 |
PQID | 2557534274 |
PQPubID | 23479 |
PageCount | 18 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_36382bcfb0fa488f8d19fd1190c7b118 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8407324 proquest_miscellaneous_2557534274 crossref_primary_10_1128_msystems_00260_21 crossref_primary_10_1128_mSystems_00260_21 asm2_journals_10_1128_mSystems_00260_21 pubmed_primary_34342535 |
PublicationCentury | 2000 |
PublicationDate | 2021-Aug-31 |
PublicationDateYYYYMMDD | 2021-08-31 |
PublicationDate_xml | – month: 08 year: 2021 text: 2021-Aug-31 day: 31 |
PublicationDecade | 2020 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: 1752 N St., N.W., Washington, DC |
PublicationTitle | mSystems |
PublicationTitleAbbrev | mSystems |
PublicationTitleAlternate | mSystems |
PublicationYear | 2021 |
Publisher | American Society for Microbiology |
Publisher_xml | – name: American Society for Microbiology |
References | Goold, HD, Kroukamp, H, Williams, TC, Paulsen, IT, Varela, C, Pretorius, IS (B21) 2017; 10 Raghunathan, AU, Pérez-Correa, JR, Agosin, E, Biegler, LT (B33) 2006; 148 Gamero, A, Tronchoni, J, Querol, A, Belloch, C (B20) 2013; 114 Camarasa, C, Grivet, J-P, Dequin, S (B40) 2003; 149 Minebois, R, Pérez-Torrado, R, Querol, A (B38) 2020; 90 Wang, G, Huang, M, Nielsen, J (B6) 2017; 48 Dikicioglu, D, Kırdar, B, Oliver, SG (B37) 2015; 11 Coleman, ST, Fang, TK, Rovinsky, SA, Turano, FJ, Moye-Rowley, WS (B44) 2001; 276 Machado, D, Herrgård, M (B62) 2014; 10 Bach, B, Sauvage, F-X, Dequin, S, Camarasa, C (B41) 2009; 60 Kelly, JM, van Dyk, SA, Dowling, LK, Pickering, GJ, Kemp, B, Inglis, DL (B49) 2020; 54 Alonso-del-Real, J, Lairón-Peris, M, Barrio, E, Querol, A (B22) 2017; 8 Schulze, U, Lidén, G, Nielsen, J, Villadsen, J (B35) 1996; 142 Hittinger, CT, Rokas, A, Bai, F-Y, Boekhout, T, Gonçalves, P, Jeffries, TW, Kominek, J, Lachance, M-A, Libkind, D, Rosa, CA, Sampaio, JP, Kurtzman, CP (B10) 2015; 35 Mara, P, Fragiadakis, G, Gkountromichos, F, Alexandraki, D (B43) 2018; 17 Lewis, NE, Hixson, KK, Conrad, TM, Lerman, JA, Charusanti, P, Polpitiya, AD, Adkins, JN, Schramm, G, Purvine, SO, Lopez-Ferrer, D, Weitz, KK, Eils, R, König, R, Smith, RD, Palsson, BØ (B19) 2010; 6 Coulter, AD, Godden, PW, Pretorius, IS (B39) 2004; 19 Su, Y, Seguinot, P, Sanchez, I, Ortiz-Julien, A, Heras, JM, Querol, A, Camarasa, C, Guillamón, JM (B56) 2020; 85 Perli, T, van der Vorm Daan, NA, Wassink, M, van den Broek, M, Pronk, JT, Daran, J-M (B4) 2021; 65 Martinez, MJ, Roy, S, Archuletta, AB, Wentzell, PD, Anna-Arriola, SS, Rodriguez, AL, Aragon, AD, Quinones, GA, Allen, C, Werner-Washburne, M (B52) 2004; 15 Crépin, L, Truong, NM, Bloem, A, Sanchez, I, Dequin, S, Camarasa, C (B50) 2017; 83 Yuan, J, Chen, X, Mishra, P, Ching, C-B (B54) 2017; 101 Cramer, AC, Vlassides, S, Block, DE (B28) 2002; 77 López-Malo, M, Querol, A, Guillamon, JM (B29) 2013; 8 Klein, M, Swinnen, S, Thevelein, JM, Nevoigt, E (B5) 2017; 19 Bühlmann, P, Gentle, JE, Härdle, WK, Mori, Y (B66) 2012 Minebois, R, Pérez-Torrado, R, Querol, A (B25) 2020; 22 Varela, C, Pizarro, F, Agosin, E (B36) 2004; 70 Liu, Q, Liu, Y, Chen, Y, Nielsen, J (B8) 2020; 65 Scott, WT, Smid, EJ, Notebaart, RA, Block, DE (B32) 2020; 8 Goffeau, A, Barrell, BG, Bussey, H, Davis, RW, Dujon, B, Feldmann, H, Galibert, F, Hoheisel, JD, Jacq, C, Johnston, M, Louis, EJ, Mewes, HW, Murakami, Y, Philippsen, P, Tettelin, H, Oliver, SG (B59) 1996; 274 Efron, B, Tibshirani, R (B67) 1988 Crépin, L, Sanchez, I, Nidelet, T, Dequin, S, Camarasa, C (B27) 2014; 13 Saitua, F, Torres, P, Pérez-Correa, JR, Agosin, E (B17) 2017; 11 Pizarro, F, Varela, C, Martabit, C, Bruno, C, Pérez-Correa, JR, Agosin, E (B34) 2007; 98 Oberhardt, MA, Palsson, BØ, Papin, JA (B11) 2009; 5 Sánchez, BJ, Zhang, C, Nilsson, A, Lahtvee, P-J, Kerkhoven, EJ, Nielsen, J (B12) 2017; 13 Varela, C, Barker, A, Tran, T, Borneman, A, Curtin, C (B23) 2017; 252 Varma, A, Palsson, BØ (B60) 1994; 60 Breiman, L (B65) 1996; 24 Querol, A, Barrio, E, Huerta, T, Ramón, D (B55) 1992; 58 van Wyk, N, Kroukamp, H, Pretorius, IS (B7) 2018; 4 Liu, Q, Yu, T, Li, X, Chen, Y, Campbell, K, Nielsen, J, Chen, Y (B2) 2019; 10 Egea, JA, Vazquez, E, Banga, JR, Marti, R (B70) 2009; 43 Lu, H, Li, F, Sánchez, BJ, Zhu, Z, Li, G, Domenzain, I, Marcišauskas, S, Anton, PM, Lappa, D, Lieven, C, Beber, ME, Sonnenschein, N, Kerkhoven, EJ, Nielsen, J (B18) 2019; 10 Rojas, V, Gil, JV, Piñaga, F, Manzanares, P (B71) 2001; 70 Sánchez, BJ, Pérez-Correa, JR, Agosin, E (B16) 2014; 25 Guo, W, Huang, Q, Feng, Y, Tan, T, Niu, S, Hou, S, Chen, Z, Du, Z-Q, Shen, Y, Fang, X (B3) 2020; 117 Crépin, L, Nidelet, T, Sanchez, I, Dequin, S, Camarasa, C (B31) 2012; 78 Tosi, E, Azzolini, M, Guzzo, F, Zapparoli, G (B24) 2009; 107 Müller-Santos, M, Koskimäki, JJ, Alves, LPS, de Souza, EM, Jendrossek, D, Pirttilä, AM (B48) 2021; 45 Lopes, H, Rocha, I (B13) 2017; 17 Liu, H, Marsafari, M, Deng, L, Xu, P (B46) 2019; 103 Schellenberger, J, Que, R, Fleming, RMT, Thiele, I, Orth, JD, Feist, AM, Zielinski, DC, Bordbar, A, Lewis, NE, Rahmanian, S, Kang, J, Hyduke, DR, Palsson, BØ (B69) 2011; 6 Steensels, J, Verstrepen, KJ (B9) 2014; 68 Hjersted, JL, Henson, MA, Mahadevan, R (B14) 2007; 97 Balsa-Canto, E, Alonso, AA, Banga, JR (B64) 2010; 4 Walter, E, Pronzato, L (B63) 1997 Vargas, FA, Pizarro, F, Pérez-Correa, JR, Agosin, E (B15) 2011; 5 Nielsen, J, Keasling, JD (B1) 2016; 164 Shopska, V, Denkova, R, Lyubenova, V, Kostov, G, Grumezescu, A, Holban, AM (B53) 2019; 5 Możejko-Ciesielska, J, Kiewisz, R (B47) 2016; 192 Cao, J, Barbosa, JM, Singh, N, Locy, RD (B42) 2013; 30 Wang, Y, Zhang, H, Lu, X, Zong, H, Zhuge, B (B26) 2019; 37 Rossignol, T, Dulau, L, Julien, A, Blondin, B (B45) 2003; 20 Otto, TD, Dillon, GP, Degrave, WS, Berriman, M (B58) 2011; 39 Mendes-Ferreira, A, del Olmo, M, García-Martínez, J, Jiménez-Martí, E, Leão, C, Mendes-Faia, A, Pérez-Ortín, JE (B51) 2007; 73 Vázquez-Lima, F, Silva, P, Barreiro, A, Martínez-Moreno, R, Morales, P, Quirós, M, González, R, Albiol, J, Ferrer, P (B30) 2014; 13 Balsa-Canto, E, Henriques, D, Gabor, A, Banga, JR (B68) 2016; 32 Orth, JD, Thiele, I, Palsson, BØ (B61) 2010; 28 Morard, M, Macías, LG, Adam, AC, Lairón-Peris, M, Pérez-Torrado, R, Toft, C, Barrio, E (B57) 2019; 10 e_1_3_2_26_2 e_1_3_2_49_2 e_1_3_2_28_2 e_1_3_2_41_2 e_1_3_2_20_2 e_1_3_2_43_2 e_1_3_2_62_2 e_1_3_2_22_2 e_1_3_2_45_2 e_1_3_2_24_2 e_1_3_2_47_2 e_1_3_2_66_2 e_1_3_2_60_2 e_1_3_2_9_2 e_1_3_2_16_2 e_1_3_2_37_2 e_1_3_2_7_2 Efron B (e_1_3_2_68_2) 1988 e_1_3_2_18_2 e_1_3_2_39_2 e_1_3_2_54_2 e_1_3_2_10_2 e_1_3_2_31_2 e_1_3_2_52_2 e_1_3_2_5_2 e_1_3_2_12_2 e_1_3_2_33_2 e_1_3_2_58_2 e_1_3_2_3_2 e_1_3_2_14_2 e_1_3_2_35_2 e_1_3_2_56_2 e_1_3_2_50_2 e_1_3_2_71_2 e_1_3_2_27_2 e_1_3_2_48_2 e_1_3_2_29_2 e_1_3_2_65_2 e_1_3_2_21_2 e_1_3_2_63_2 e_1_3_2_23_2 e_1_3_2_44_2 e_1_3_2_69_2 e_1_3_2_25_2 e_1_3_2_46_2 e_1_3_2_67_2 e_1_3_2_61_2 Coulter AD (e_1_3_2_40_2) 2004; 19 e_1_3_2_15_2 e_1_3_2_38_2 e_1_3_2_8_2 e_1_3_2_17_2 e_1_3_2_59_2 e_1_3_2_6_2 e_1_3_2_19_2 Walter E (e_1_3_2_64_2) 1997 e_1_3_2_30_2 Bach B (e_1_3_2_42_2) 2009; 60 e_1_3_2_53_2 e_1_3_2_32_2 e_1_3_2_51_2 e_1_3_2_11_2 e_1_3_2_34_2 e_1_3_2_57_2 e_1_3_2_4_2 e_1_3_2_13_2 e_1_3_2_36_2 e_1_3_2_55_2 e_1_3_2_2_2 e_1_3_2_72_2 e_1_3_2_70_2 e_1_3_2_42_2 |
References_xml | – volume: 5 start-page: 320 year: 2009 ident: B11 article-title: Applications of genome-scale metabolic reconstructions publication-title: Mol Syst Biol doi: 10.1038/msb.2009.77 contributor: fullname: Papin, JA – volume: 10 start-page: 4976 year: 2019 ident: B2 article-title: Rewiring carbon metabolism in yeast for high level production of aromatic chemicals publication-title: Nat Commun doi: 10.1038/s41467-019-12961-5 contributor: fullname: Chen, Y – volume: 39 year: 2011 ident: B58 article-title: RATT: Rapid Annotation Transfer Tool publication-title: Nucleic Acids Res doi: 10.1093/nar/gkq1268 contributor: fullname: Berriman, M – volume: 4 start-page: 54 year: 2018 ident: B7 article-title: The smell of synthetic biology: engineering strategies for aroma compound production in yeast publication-title: Fermentation doi: 10.3390/fermentation4030054 contributor: fullname: Pretorius, IS – volume: 11 start-page: 27 year: 2017 ident: B17 article-title: Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris publication-title: BMC Syst Biol doi: 10.1186/s12918-017-0408-2 contributor: fullname: Agosin, E – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: B65 article-title: Bagging predictors publication-title: Mach Learn doi: 10.1007/BF00058655 contributor: fullname: Breiman, L – volume: 19 start-page: 878 year: 2017 end-page: 893 ident: B5 article-title: Glycerol metabolism and transport in yeast and fungi: established knowledge and ambiguities publication-title: Environ Microbiol doi: 10.1111/1462-2920.13617 contributor: fullname: Nevoigt, E – volume: 8 start-page: 150 year: 2017 ident: B22 article-title: Effect of temperature on the prevalence of Saccharomyces non cerevisiae species against a S. cerevisiae wine strain in wine fermentation: competition, physiological fitness, and influence in final wine composition publication-title: Frontiers Microbiol doi: 10.3389/fmicb.2017.00150 contributor: fullname: Querol, A – volume: 68 start-page: 61 year: 2014 end-page: 80 ident: B9 article-title: Taming wild yeast: potential of conventional and nonconventional yeasts in industrial fermentations publication-title: Annu Rev Microbiol doi: 10.1146/annurev-micro-091213-113025 contributor: fullname: Verstrepen, KJ – volume: 60 start-page: 508 year: 2009 end-page: 516 ident: B41 article-title: Role of γ-aminobutyric acid as a source of nitrogen and succinate in wine publication-title: Am J Enol Vit contributor: fullname: Camarasa, C – volume: 4 start-page: 11 year: 2010 ident: B64 article-title: An iterative identification procedure for dynamic modeling of biochemical networks publication-title: BMC Syst Biol doi: 10.1186/1752-0509-4-11 contributor: fullname: Banga, JR – year: 1997 ident: B63 publication-title: Identification of parametric models from experimental data. ;Springer ;Berlin, Germany contributor: fullname: Pronzato, L – volume: 149 start-page: 2669 year: 2003 end-page: 2678 ident: B40 article-title: Investigation by 13C-NMR and tricarboxylic acid (TCA) deletion mutant analysis of pathways for succinate formation in Saccharomyces cerevisiae during anaerobic fermentation publication-title: Microbiology (Reading) doi: 10.1099/mic.0.26007-0 contributor: fullname: Dequin, S – start-page: 985 year: 2012 end-page: 1022 ident: B66 article-title: Bagging, boosting and ensemble methods publication-title: Handbook of computational statistics: concepts and methods. ;Springer ;Berlin, Germany contributor: fullname: Mori, Y – volume: 6 start-page: 390 year: 2010 ident: B19 article-title: Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models publication-title: Mol Syst Biol doi: 10.1038/msb.2010.47 contributor: fullname: Palsson, BØ – volume: 22 start-page: 3700 year: 2020 end-page: 3721 ident: B25 article-title: Metabolome segregation of four strains of Saccharomyces cerevisiae, S. uvarum and S. kudriavzevii conducted under low temperature oenological conditions publication-title: Environ Microbiol doi: 10.1111/1462-2920.15135 contributor: fullname: Querol, A – volume: 117 start-page: 2410 year: 2020 end-page: 2419 ident: B3 article-title: Rewiring central carbon metabolism for tyrosol and salidroside production in Saccharomyces cerevisiae publication-title: Biotechnol Bioeng doi: 10.1002/bit.27370 contributor: fullname: Fang, X – volume: 8 year: 2013 ident: B29 article-title: Metabolomic comparison of Saccharomyces cerevisiae and the cryotolerant species S. bayanus var. uvarum and S. kudriavzevii during wine fermentation at low temperature publication-title: PLoS One doi: 10.1371/journal.pone.0060135 contributor: fullname: Guillamon, JM – volume: 10 start-page: 82 year: 2019 ident: B57 article-title: Aneuploidy and ethanol tolerance in Saccharomyces cerevisiae publication-title: Front Genet doi: 10.3389/fgene.2019.00082 contributor: fullname: Barrio, E – volume: 107 start-page: 210 year: 2009 end-page: 218 ident: B24 article-title: Evidence of different fermentation behaviours of two indigenous strains of Saccharomyces cerevisiae and Saccharomyces uvarum isolated from amarone wine publication-title: J Appl Microbiol doi: 10.1111/j.1365-2672.2009.04196.x contributor: fullname: Zapparoli, G – volume: 70 start-page: 283 year: 2001 end-page: 289 ident: B71 article-title: Studies on acetate ester production by non-Saccharomyces wine yeasts publication-title: Int J Food Microbiol doi: 10.1016/s0168-1605(01)00552-9 contributor: fullname: Manzanares, P – volume: 32 start-page: 3357 year: 2016 end-page: 3359 ident: B68 article-title: AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw411 contributor: fullname: Banga, JR – volume: 65 start-page: 11 year: 2021 end-page: 29 ident: B4 article-title: Engineering heterologous molybdenum-cofactor-biosynthesis and nitrate-assimilation pathways enables nitrate utilization by Saccharomyces cerevisiae publication-title: Metab Eng doi: 10.1016/j.ymben.2021.02.004 contributor: fullname: Daran, J-M – volume: 45 start-page: fuaa058 year: 2021 ident: B48 article-title: The protective role of PHB and its degradation products against stress situations in bacteria publication-title: FEMS Microbiol Rev doi: 10.1093/femsre/fuaa058 contributor: fullname: Pirttilä, AM – volume: 83 year: 2017 ident: B50 article-title: Management of multiple nitrogen sources during wine fermentation by Saccharomyces cerevisiae publication-title: Appl Environ Microbiol doi: 10.1128/AEM.02617-16 contributor: fullname: Camarasa, C – volume: 15 start-page: 5295 year: 2004 end-page: 5305 ident: B52 article-title: Genomic analysis of stationary-phase and exit in Saccharomyces cerevisiae: gene expression and identification of novel essential genes publication-title: Mol Biol Cell doi: 10.1091/mbc.e03-11-0856 contributor: fullname: Werner-Washburne, M – volume: 30 start-page: 279 year: 2013 end-page: 289 ident: B42 article-title: GABA transaminases from Saccharomyces cerevisiae and Arabidopsis thaliana complement function in cytosol and mitochondria publication-title: Yeast doi: 10.1002/yea.2962 contributor: fullname: Locy, RD – volume: 5 start-page: 529 year: 2019 end-page: 575 ident: B53 article-title: Kinetic characteristics of alcohol fermentation in brewing: state of art and control of the fermentation process publication-title: The science of beverages ;Fermented beverages. ;Elsevier ;Duxford, United Kingdom contributor: fullname: Holban, AM – volume: 10 start-page: 3586 year: 2019 ident: B18 article-title: A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism publication-title: Nat Commun doi: 10.1038/s41467-019-11581-3 contributor: fullname: Nielsen, J – volume: 54 start-page: 199 year: 2020 end-page: 211 ident: B49 article-title: Ssaccharomyces uvarum yeast isolate consumes acetic acid during fermentation of high sugar juice and juice with high starting volatile acidity publication-title: Oeno One doi: 10.20870/oeno-one.2020.54.2.2594 contributor: fullname: Inglis, DL – volume: 5 start-page: 75 year: 2011 ident: B15 article-title: Expanding a dynamic flux balance model of yeast fermentation to genome-scale publication-title: BMC Syst Biol doi: 10.1186/1752-0509-5-75 contributor: fullname: Agosin, E – volume: 17 start-page: 170 year: 2018 ident: B43 article-title: The pleiotropic effects of the glutamate dehydrogenase (GDH) pathway in Saccharomyces cerevisiae publication-title: Microb Cell Fact doi: 10.1186/s12934-018-1018-4 contributor: fullname: Alexandraki, D – volume: 148 start-page: 251 year: 2006 end-page: 270 ident: B33 article-title: Parameter estimation in metabolic flux balance models for batch fermentation—formulation & solution using differential variational inequalities (DVIs) publication-title: Ann Oper Res doi: 10.1007/s10479-006-0086-8 contributor: fullname: Biegler, LT – volume: 58 start-page: 2948 year: 1992 end-page: 2953 ident: B55 article-title: Molecular monitoring of wine fermentations conducted by active dry yeast strains publication-title: Appl Environ Microbiol doi: 10.1128/aem.58.9.2948-2953.1992 contributor: fullname: Ramón, D – volume: 103 start-page: 3167 year: 2019 end-page: 3179 ident: B46 article-title: Understanding lipogenesis by dynamically profiling transcriptional activity of lipogenic promoters in Yarrowia lipolytica publication-title: Appl Microbiol Biotechnol doi: 10.1007/s00253-019-09664-8 contributor: fullname: Xu, P – volume: 10 year: 2014 ident: B62 article-title: Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1003580 contributor: fullname: Herrgård, M – volume: 11 start-page: 1690 year: 2015 end-page: 1701 ident: B37 article-title: Biomass composition: the “elephant in the room” of metabolic modelling publication-title: Metabolomics doi: 10.1007/s11306-015-0819-2 contributor: fullname: Oliver, SG – volume: 65 start-page: 65 year: 2020 end-page: 74 ident: B8 article-title: Current state of aromatics production using yeast: achievements and challenges publication-title: Curr Opin Biotechnol doi: 10.1016/j.copbio.2020.01.008 contributor: fullname: Nielsen, J – volume: 37 start-page: 403 year: 2019 end-page: 409 ident: B26 article-title: Advances in 2-phenylethanol production from engineered microorganisms publication-title: Biotechnol Adv doi: 10.1016/j.biotechadv.2019.02.005 contributor: fullname: Zhuge, B – volume: 70 start-page: 3392 year: 2004 end-page: 3400 ident: B36 article-title: Biomass content governs fermentation rate in nitrogen-deficient wine musts publication-title: Appl Environ Microbiol doi: 10.1128/AEM.70.6.3392-3400.2004 contributor: fullname: Agosin, E – volume: 101 start-page: 465 year: 2017 end-page: 474 ident: B54 article-title: Metabolically engineered Saccharomyces cerevisiae for enhanced isoamyl alcohol production publication-title: Appl Microbiol Biotechnol doi: 10.1007/s00253-016-7970-1 contributor: fullname: Ching, C-B – volume: 142 start-page: 2299 year: 1996 end-page: 2310 ident: B35 article-title: Physiological effects of nitrogen starvation in an anaerobic batch culture of Saccharomyces cerevisiae publication-title: Microbiology (Reading) doi: 10.1099/13500872-142-8-2299 contributor: fullname: Villadsen, J – volume: 274 start-page: 546 year: 1996 end-page: 567 ident: B59 article-title: Life with 6000 genes publication-title: Science doi: 10.1126/science.274.5287.546 contributor: fullname: Oliver, SG – volume: 20 start-page: 1369 year: 2003 end-page: 1385 ident: B45 article-title: Genome-wide monitoring of wine yeast gene expression during alcoholic fermentation publication-title: Yeast doi: 10.1002/yea.1046 contributor: fullname: Blondin, B – volume: 17 start-page: fox050 year: 2017 ident: B13 article-title: Genome-scale modeling of yeast: chronology, applications and critical perspectives publication-title: FEMS Yeast Res doi: 10.1093/femsyr/fox050 contributor: fullname: Rocha, I – volume: 13 start-page: 109 year: 2014 ident: B27 article-title: Efficient ammonium uptake and mobilization of vacuolar arginine by Saccharomyces cerevisiae wine strains during wine fermentation publication-title: Microb Cell Fact doi: 10.1186/s12934-014-0109-0 contributor: fullname: Camarasa, C – volume: 114 start-page: 1405 year: 2013 end-page: 1414 ident: B20 article-title: Production of aroma compounds by cryotolerant Saccharomyces species and hybrids at low and moderate fermentation temperatures publication-title: J Appl Microbiol doi: 10.1111/jam.12126 contributor: fullname: Belloch, C – volume: 35 start-page: 100 year: 2015 end-page: 109 ident: B10 article-title: Genomics and the making of yeast biodiversity publication-title: Curr Opin Genet Dev doi: 10.1016/j.gde.2015.10.008 contributor: fullname: Kurtzman, CP – volume: 164 start-page: 1185 year: 2016 end-page: 1197 ident: B1 article-title: Engineering cellular metabolism publication-title: Cell doi: 10.1016/j.cell.2016.02.004 contributor: fullname: Keasling, JD – volume: 6 start-page: 1290 year: 2011 end-page: 1307 ident: B69 article-title: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0 publication-title: Nat Protoc doi: 10.1038/nprot.2011.308 contributor: fullname: Palsson, BØ – volume: 85 start-page: 103287 year: 2020 ident: B56 article-title: Nitrogen sources preferences of non-Saccharomyces yeasts to sustain growth and fermentation under winemaking conditions publication-title: Food Microbiol doi: 10.1016/j.fm.2019.103287 contributor: fullname: Guillamón, JM – volume: 97 start-page: 1190 year: 2007 end-page: 1204 ident: B14 article-title: Genome‐scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed‐batch culture publication-title: Biotechnol Bioeng doi: 10.1002/bit.21332 contributor: fullname: Mahadevan, R – volume: 43 start-page: 175 year: 2009 end-page: 190 ident: B70 article-title: Improved scatter search for the global optimization of computationally expensive dynamic models publication-title: J Glob Optim doi: 10.1007/s10898-007-9172-y contributor: fullname: Marti, R – volume: 77 start-page: 49 year: 2002 end-page: 60 ident: B28 article-title: Kinetic model for nitrogen-limited wine fermentations publication-title: Biotechnol Bioeng doi: 10.1002/bit.10133 contributor: fullname: Block, DE – year: 1988 ident: B67 publication-title: An introduction to the bootstrap. ;Chapman & Hall ;New York, NY contributor: fullname: Tibshirani, R – volume: 13 start-page: 935 year: 2017 ident: B12 article-title: Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints publication-title: Mol Syst Biol doi: 10.15252/msb.20167411 contributor: fullname: Nielsen, J – volume: 78 start-page: 8102 year: 2012 end-page: 8111 ident: B31 article-title: Sequential use of nitrogen compounds by Saccharomyces cerevisiae during wine fermentation: a model based on kinetic and regulation characteristics of nitrogen permeases publication-title: Appl Environ Microbiol doi: 10.1128/AEM.02294-12 contributor: fullname: Camarasa, C – volume: 13 start-page: 85 year: 2014 ident: B30 article-title: Use of chemostat cultures mimicking different phases of wine fermentations as a tool for quantitative physiological analysis publication-title: Microb Cell Fact doi: 10.1186/1475-2859-13-85 contributor: fullname: Ferrer, P – volume: 276 start-page: 244 year: 2001 end-page: 250 ident: B44 article-title: Expression of a glutamate decarboxylase homologue is required for normal oxidative stress tolerance in Saccharomyces cerevisiae publication-title: J Biol Chem doi: 10.1074/jbc.M007103200 contributor: fullname: Moye-Rowley, WS – volume: 28 start-page: 245 year: 2010 end-page: 248 ident: B61 article-title: What is flux balance analysis? publication-title: Nat Biotechnol doi: 10.1038/nbt.1614 contributor: fullname: Palsson, BØ – volume: 25 start-page: 159 year: 2014 end-page: 173 ident: B16 article-title: Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization publication-title: Metab Eng doi: 10.1016/j.ymben.2014.07.004 contributor: fullname: Agosin, E – volume: 60 start-page: 3724 year: 1994 end-page: 3731 ident: B60 article-title: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 publication-title: Appl Environ Microbiol doi: 10.1128/aem.60.10.3724-3731.1994 contributor: fullname: Palsson, BØ – volume: 252 start-page: 1 year: 2017 end-page: 9 ident: B23 article-title: Sensory profile and volatile aroma composition of reduced alcohol merlot wines fermented with Metschnikowia pulcherrima and Saccharomyces uvarum publication-title: Int J Food Microbiol doi: 10.1016/j.ijfoodmicro.2017.04.002 contributor: fullname: Curtin, C – volume: 10 start-page: 264 year: 2017 end-page: 278 ident: B21 article-title: Yeast’s balancing act between ethanol and glycerol production in low-alcohol wines publication-title: Microb Biotechnol doi: 10.1111/1751-7915.12488 contributor: fullname: Pretorius, IS – volume: 73 start-page: 5363 year: 2007 end-page: 5369 ident: B51 article-title: Saccharomyces cerevisiae signature genes for predicting nitrogen deficiency during alcoholic fermentation publication-title: Appl Environ Microbiol doi: 10.1128/AEM.01029-07 contributor: fullname: Pérez-Ortín, JE – volume: 8 start-page: 1195 year: 2020 ident: B32 article-title: Curation and analysis of a Saccharomyces cerevisiae genome-scale metabolic model for predicting production of sensory impact molecules under enological conditions publication-title: Processes doi: 10.3390/pr8091195 contributor: fullname: Block, DE – volume: 19 start-page: 16 year: 2004 end-page: 25 ident: B39 article-title: Succinic acid—how is it formed, what is its effect on titratable acidity, and what factors influence its concentration in wine? publication-title: Wine Ind J contributor: fullname: Pretorius, IS – volume: 48 start-page: 77 year: 2017 end-page: 84 ident: B6 article-title: Exploring the potential of Saccharomyces cerevisiae for biopharmaceutical protein production publication-title: Curr Opin Biotechnol doi: 10.1016/j.copbio.2017.03.017 contributor: fullname: Nielsen, J – volume: 90 start-page: 103484 year: 2020 ident: B38 article-title: A time course metabolism comparison among Saccharomyces cerevisiae, S. uvarum and S. kudriavzevii species in wine fermentation publication-title: Food Microbiol doi: 10.1016/j.fm.2020.103484 contributor: fullname: Querol, A – volume: 98 start-page: 986 year: 2007 end-page: 998 ident: B34 article-title: Coupling kinetic expressions and metabolic networks for predicting wine fermentations publication-title: Biotechnol Bioeng doi: 10.1002/bit.21494 contributor: fullname: Agosin, E – volume: 192 start-page: 271 year: 2016 end-page: 282 ident: B47 article-title: Bacterial polyhydroxyalkanoates: still fabulous? publication-title: Microbiol Res doi: 10.1016/j.micres.2016.07.010 contributor: fullname: Kiewisz, R – ident: e_1_3_2_59_2 doi: 10.1093/nar/gkq1268 – ident: e_1_3_2_18_2 doi: 10.1186/s12918-017-0408-2 – ident: e_1_3_2_6_2 doi: 10.1111/1462-2920.13617 – ident: e_1_3_2_65_2 doi: 10.1186/1752-0509-4-11 – volume: 60 start-page: 508 year: 2009 ident: e_1_3_2_42_2 article-title: Role of γ-aminobutyric acid as a source of nitrogen and succinate in wine publication-title: Am J Enol Vit doi: 10.5344/ajev.2009.60.4.508 contributor: fullname: Bach B – ident: e_1_3_2_57_2 doi: 10.1016/j.fm.2019.103287 – ident: e_1_3_2_9_2 doi: 10.1016/j.copbio.2020.01.008 – ident: e_1_3_2_11_2 doi: 10.1016/j.gde.2015.10.008 – ident: e_1_3_2_36_2 doi: 10.1099/13500872-142-8-2299 – ident: e_1_3_2_19_2 doi: 10.1038/s41467-019-11581-3 – ident: e_1_3_2_38_2 doi: 10.1007/s11306-015-0819-2 – ident: e_1_3_2_55_2 doi: 10.1007/s00253-016-7970-1 – ident: e_1_3_2_22_2 doi: 10.1111/1751-7915.12488 – ident: e_1_3_2_41_2 doi: 10.1099/mic.0.26007-0 – ident: e_1_3_2_5_2 doi: 10.1016/j.ymben.2021.02.004 – ident: e_1_3_2_63_2 doi: 10.1371/journal.pcbi.1003580 – ident: e_1_3_2_60_2 doi: 10.1126/science.274.5287.546 – ident: e_1_3_2_32_2 doi: 10.1128/AEM.02294-12 – ident: e_1_3_2_28_2 doi: 10.1186/s12934-014-0109-0 – ident: e_1_3_2_23_2 doi: 10.3389/fmicb.2017.00150 – ident: e_1_3_2_15_2 doi: 10.1002/bit.21332 – ident: e_1_3_2_12_2 doi: 10.1038/msb.2009.77 – volume-title: An introduction to the bootstrap. year: 1988 ident: e_1_3_2_68_2 contributor: fullname: Efron B – ident: e_1_3_2_47_2 doi: 10.1007/s00253-019-09664-8 – ident: e_1_3_2_46_2 doi: 10.1002/yea.1046 – ident: e_1_3_2_10_2 doi: 10.1146/annurev-micro-091213-113025 – ident: e_1_3_2_34_2 doi: 10.1007/s10479-006-0086-8 – ident: e_1_3_2_2_2 doi: 10.1016/j.cell.2016.02.004 – ident: e_1_3_2_37_2 doi: 10.1128/AEM.70.6.3392-3400.2004 – ident: e_1_3_2_7_2 doi: 10.1016/j.copbio.2017.03.017 – ident: e_1_3_2_8_2 doi: 10.3390/fermentation4030054 – ident: e_1_3_2_50_2 doi: 10.20870/oeno-one.2020.54.2.2594 – ident: e_1_3_2_3_2 doi: 10.1038/s41467-019-12961-5 – ident: e_1_3_2_26_2 doi: 10.1111/1462-2920.15135 – ident: e_1_3_2_58_2 doi: 10.3389/fgene.2019.00082 – ident: e_1_3_2_31_2 doi: 10.1186/1475-2859-13-85 – ident: e_1_3_2_54_2 doi: 10.1016/B978-0-12-815271-3.00013-0 – ident: e_1_3_2_48_2 doi: 10.1016/j.micres.2016.07.010 – ident: e_1_3_2_39_2 doi: 10.1016/j.fm.2020.103484 – ident: e_1_3_2_25_2 doi: 10.1111/j.1365-2672.2009.04196.x – ident: e_1_3_2_51_2 doi: 10.1128/AEM.02617-16 – volume-title: Identification of parametric models from experimental data. year: 1997 ident: e_1_3_2_64_2 contributor: fullname: Walter E – ident: e_1_3_2_45_2 doi: 10.1074/jbc.M007103200 – ident: e_1_3_2_14_2 doi: 10.1093/femsyr/fox050 – ident: e_1_3_2_17_2 doi: 10.1016/j.ymben.2014.07.004 – ident: e_1_3_2_16_2 doi: 10.1186/1752-0509-5-75 – ident: e_1_3_2_56_2 doi: 10.1128/aem.58.9.2948-2953.1992 – ident: e_1_3_2_53_2 doi: 10.1091/mbc.e03-11-0856 – ident: e_1_3_2_61_2 doi: 10.1128/aem.60.10.3724-3731.1994 – ident: e_1_3_2_62_2 doi: 10.1038/nbt.1614 – ident: e_1_3_2_13_2 doi: 10.15252/msb.20167411 – ident: e_1_3_2_27_2 doi: 10.1016/j.biotechadv.2019.02.005 – ident: e_1_3_2_72_2 doi: 10.1016/s0168-1605(01)00552-9 – ident: e_1_3_2_66_2 doi: 10.1007/BF00058655 – ident: e_1_3_2_29_2 doi: 10.1002/bit.10133 – ident: e_1_3_2_44_2 doi: 10.1186/s12934-018-1018-4 – ident: e_1_3_2_20_2 doi: 10.1038/msb.2010.47 – ident: e_1_3_2_67_2 doi: 10.1007/978-3-642-21551-3_33 – ident: e_1_3_2_69_2 doi: 10.1093/bioinformatics/btw411 – ident: e_1_3_2_4_2 doi: 10.1002/bit.27370 – ident: e_1_3_2_35_2 doi: 10.1002/bit.21494 – ident: e_1_3_2_43_2 doi: 10.1002/yea.2962 – ident: e_1_3_2_70_2 doi: 10.1038/nprot.2011.308 – ident: e_1_3_2_21_2 doi: 10.1111/jam.12126 – ident: e_1_3_2_71_2 doi: 10.1007/s10898-007-9172-y – ident: e_1_3_2_30_2 doi: 10.1371/journal.pone.0060135 – volume: 19 start-page: 16 year: 2004 ident: e_1_3_2_40_2 article-title: Succinic acid—how is it formed, what is its effect on titratable acidity, and what factors influence its concentration in wine? publication-title: Wine Ind J contributor: fullname: Coulter AD – ident: e_1_3_2_24_2 doi: 10.1016/j.ijfoodmicro.2017.04.002 – ident: e_1_3_2_52_2 doi: 10.1128/AEM.01029-07 – ident: e_1_3_2_33_2 doi: 10.3390/pr8091195 – ident: e_1_3_2_49_2 doi: 10.1093/femsre/fuaa058 – ident: e_1_3_2_42_2 doi: 10.5344/ajev.2009.60.4.508 |
SSID | ssj0001637129 |
Score | 2.356154 |
Snippet | Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and... Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors,... Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and... |
SourceID | doaj pubmedcentral proquest crossref asm2 pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | e0026021 |
SubjectTerms | Research Article |
SummonAdditionalLinks | – databaseName: Directory of Open Access Journals dbid: DOA link: http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3faxQxEA71QPBF_FF1rZYUBEGIvU12k93H1uu1T33wLOhT2CQTroXbLe4dtn-I_6-TZO_oVUEQ3469kA2Zycw3m-T7CHlXYI5p6soxZ6VhBZYMrFalY8p6l9cgSohah2czdf61mpwEmpyN1Fc4E5bogdPEHQp0EG6sN2PfoLP5CrvAbjCPWWUQHcfoO5Z3iqn4dUUKhZls2MbEGHy4GBjAP0YWLRa4QUdNv-Bb-SjS9v8Ja94_MnknB02fkMcDeKRHadBPyQ60z8jDJCd5-5z8PKLxPu31HFNT-tmZqxTR6CRJz9NTaLsFsBnaBtsEHRw6m3c_ejoZpFKW9DO47oYeh0OPFjMbjYJE9FtQ-aFRrx562nmK0JHOGhvubXWLW4w3oe9VTy9bOsV4P1xqanfJxfTky6czNsgusKYs5JJJq3xZugIaKQO9vsNF7uqx4U6CKgHxIaI8bivvGovTXNXGKJmDBOFlbV0uXpBR27XwilApxNiHjTpfiMLXaEdAPFOY3ABHaGMy8j7YQA_rptexJOGVXltLR2tpnmfkw9pM-jrxcPxj4_63xsfB6puGgW87PkAv1IMX6r95YUYO1j6jcX2GTZemhW7VayzZsCIssPjPyMvkQ5tXCZwVXooyI2rLu7bGsv1PezmPHOBYlyvEwq__x-D3yCMeTurEL-VvyGj5fQVvyYPerfbjqvoF0SgpIQ priority: 102 providerName: Directory of Open Access Journals |
Title | A Multiphase Multiobjective Dynamic Genome-Scale Model Shows Different Redox Balancing among Yeast Species of the Saccharomyces Genus in Fermentation |
URI | https://www.ncbi.nlm.nih.gov/pubmed/34342535 https://journals.asm.org/doi/10.1128/mSystems.00260-21 https://search.proquest.com/docview/2557534274 https://pubmed.ncbi.nlm.nih.gov/PMC8407324 https://doaj.org/article/36382bcfb0fa488f8d19fd1190c7b118 |
Volume | 6 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFLZoJSReEHfCYDISEhKS28ZO7ORxW1f2AkIUJHiy4ttaRJxqaTX2Q_i_HDvJtAJPe6tSJ7Fybt-xj8-H0JsMYkxVFoYYzRXJIGUgpcgNEdqZtLQst5Hr8GwpPn4r5qehTU4-nIWJRftarSf-Zz3x61WsrdzUejrUiU0_fTiBpEQAEJiO0Aiw4Y0UPS6scCYgiPU7mOB-p3Xf_HsSG2gRGthhWMZAWwPJ27hqa7oXlWLz_v8hzr8LJ29EosUDdL-HkPiom-pDdMf6R-huRyp59Rj9PsLxVO1mBQGq-9moH51fw_OOgB6_t76pLVmChGBMYMPBy1Vz2eJ5T5iyxZ-taX7h41D6qCG-4UhLhL8Hrh8cWettixuHAUDiZaXD6a2mvgKvE569a_Ha4wV4_f5ok3-Cvi5Ov5yckZ58gVR5xreEa-Hy3GS24jw02Tdg6qacKWq4FbkFlAhYj-rCmUrDFy9KpQRPLbfM8VKblD1FY994-xxhztjMhe06Bx_dlVRpC6gmU6myFACOStDbIAPZW08rY2JCCzkITkbBSZom6N0gJrnpunHccnD7z-DjIPXrgaHrdrzQXJzLXvckA2cFs3dq5ipwfK4AdQaVBkylhYJMLUGvB52RYKVh66Xyttm1EhI3yAszKrIEPet06PpVgyomSOxp195c9v8Bw4idwHtDeHHrOw_QPRqKdOIi-Us03l7s7Cs0as3uMK5OHEbb-gPi1Cpp |
link.rule.ids | 230,315,729,782,786,866,887,2106,27933,27934,53800,53802 |
linkProvider | National Library of Medicine |
linkToHtml | http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFLZYEYIX7mPhaiQkJKS0iZ3YyeO2rhSxTYgOCZ6s-EaLlqRaWsF-CP-XYyeZVuCpb1Hi3HRu37GPz4fQmwRiTJFnOtSKyTCBlCHMeapDrqyOc0NT47kOpzN--jUbH7k2OWm_F8YX7Su5GFbn5bBazH1t5bJUo75ObPTp5BCSEg5AYLSDboK9RtG1JN1PrTDKIYx1a5jggEdl1_576FtohcTxw9CEgr46mrdB0ZRkIy759v3_w5x_l05ei0WTe1v-xX10twOfeL-9_ADdMNVDdKulo7x8hH7vY78fdzmH0NYe1vJH6xHxuKWux-9NVZcmnIFsYYzj0cGzef2zweOOamWFPxtd_8IHrmhSQWTEntAIf3MsQdjz3ZsG1xYD9MSzQrl9X3V5Cf7KPXvd4EWFJxAvuk1R1WP0ZXJ0djgNO9qGsEgTtgqZ4jZNdWIKxlx7fg1OQueRJJoZnhrAl4ASicqsLhRIKsul5Cw2zFDLcqVjuosGVV2ZPYQZpZF1C30WhGVzIpUBPJTIWBoC0EgG6K2TnejsrhE-pSGZ6AUuvMAFiQP0rhevWLZ9PLYc3Pwz-MBpy9VA16_bn6gvvotO3oKCm4OvtzKyBbhMm4EhgDEAGlNcQo4XoNe9rgmwb7doU1SmXjcCUj7IKBPCkwA9aXXv6lW9CgeIb2jlxrdsXgFl9D3EO-V7uvWdr9Dt6dnJsTj-cPrxGbpDXKmPn2p_jgari7V5gXYavX7pLfMP_sE--A |
linkToPdf | http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9NAEB7RIBAv3Ic5FwkJCclxvLZ37ce2aSgCqoqABE8r70VS1XZUJ4L-EP4vs2snaoCn8mbFk8TWXN_szs4H8CrFHFMWuQ61YjJMsWQIC57pkCur48IkmfFch4dTfvQ1Hx-4MTkbqi_ftK_kfFifVsN6PvO9lYtKRes-sej44z4WJRyBQLTQNtqBq-izI3qhUPfLKyzhmMr6fUwMwlHVjwAf-jFaIXUcMUmaoM06qrdB2VZ0Kzf5Ef7_wp1_tk9eyEeTW__xJrfhZg9CyW4ncgeumPouXOtoKc_vwa9d4s_lLmaY4rrLRp50kZGMOwp78tbUTWXCKeoYZRyfDpnOmh8tGfeUK0vyyejmJ9lzzZMKMyTxxEbkm2MLIp733rSksQQhKJmWyp3_aqpzjFvut1ctmddkgnmjPxxV34cvk4PP-4dhT98QllnKliFT3GaZTk3JmBvTrzFY6GIkqWaGZwZxJqJFqnKrS4XaygspOYsNM4llhdJx8gAGdVObR0BYkoys2_CzqDBbUKkM4qJUxtJQhEgygNdOf6L3v1b40obmYq104ZUuaBzAm7WKxaKb53FJ4fYv4T1nMRtBN7fbf9CcfRe9zkWC4Q6f3sqRLTF02hwdAp0CUZniEmu9AF6u7U2gn7vNm7I2zaoVWPphZZlSngbwsLO_zV-tzTgAvmWZW8-yfQcN0s8S7w3w8aW_-QKuH48n4sO7o_dP4AZ1HT9-xf0pDJZnK_MMdlq9eu6d8zfhSkF4 |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Multiphase+Multiobjective+Dynamic+Genome-Scale+Model+Shows+Different+Redox+Balancing+among+Yeast+Species+of+the+Saccharomyces+Genus+in+Fermentation&rft.jtitle=mSystems&rft.au=Henriques%2C+David&rft.au=Minebois%2C+Romain&rft.au=Mendoza%2C+Sebasti%C3%A1n+N&rft.au=Mac%C3%ADas%2C+Laura+G&rft.date=2021-08-31&rft.issn=2379-5077&rft.eissn=2379-5077&rft.volume=6&rft.issue=4&rft.spage=e0026021&rft.epage=e0026021&rft_id=info:doi/10.1128%2FmSystems.00260-21&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2379-5077&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2379-5077&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2379-5077&client=summon |