酵母基因组规模模型进展及发展趋势

李永珠; 陈禹

doi:10.12211/2096-8280.2024-084

您当前的位置：

首页 >

文章列表页 >

酵母基因组规模模型进展及发展趋势

特约评述 | 更新时间：2025-07-07

- 酵母基因组规模模型进展及发展趋势
- Advances and prospects in genome-scale models of yeast
- 合成生物学 2025年6卷第3期页码：585-602
- 作者机构：
  
  1.中国科学院深圳先进技术研究院，定量合成生物学全国重点实验室，深圳合成生物学创新研究院，广东深圳 518055
  2.中国科学院大学，北京 100049
- 作者简介：
  
  [ "李永珠（2001—），女，硕士研究生。研究方向为代谢网络建模。E-mail：yz.li4@siat.ac.cn" ]
  [ "陈禹（1990—），男，研究员，博士生导师。研究方向为系统生物学和定量合成生物学，致力于结合代谢网络建模和生物数据分析加速合成生物系统理性设计。E-mail：y.chen3@siat.ac.cn" ]
- 基金信息：
  
  国家重点研发计划(2023YFA0913900)
- DOI：10.12211/2096-8280.2024-084
  中图分类号： Q819
- 收稿：2024-12-02，
  
  修回：2025-02-20，
  
  纸质出版：2025-06-30
- 稿件说明：
移动端阅览
李永珠，陈禹. 酵母基因组规模模型进展及发展趋势［J］. 合成生物学， 2025， 6（3）： 585-602

LI Yongzhu， CHEN Yu. Advances and prospects in genome-scale models of yeast［J］. Synthetic Biology Journal， 2025， 6（3）： 585-602
李永珠，陈禹. 酵母基因组规模模型进展及发展趋势［J］. 合成生物学， 2025， 6（3）： 585-602 DOI： 10.12211/2096-8280.2024-084.

LI Yongzhu， CHEN Yu. Advances and prospects in genome-scale models of yeast［J］. Synthetic Biology Journal， 2025， 6（3）： 585-602 DOI： 10.12211/2096-8280.2024-084.

摘要

酵母作为常用的真核模式生物，在合成生物学和系统生物学研究中具有重要地位。然而，由于其代谢系统较为复杂，进行代谢网络研究和设计时存在一定困难，因此，研究人员提出了基因组规模的建模方法，利用基因组序列及注释信息，整合细胞内复杂的代谢反应和细胞过程，模拟细胞系统各部分的相互作用，得到对应的表型、功能和行为，辅助寻找代谢工程改造靶点，为理解复杂细胞系统提供了强有力的工具。本文介绍了基因组规模模型中传统代谢模型及整合多种生理学约束和多种细胞过程的模型的构建和分析方法，回顾了酵母属中多种酵母基因组规模模型的发展历程及主要应用，并基于此分析了当前酵母基因组规模模型研究中面临的主要问题，提出了提升模型准确率以及未来进一步优化模型的方法和趋势。

Abstract

Yeasts

particularly

Saccharomyces cerevisiae

are widely used eukaryotic organisms with relatively clear cellular structures and metabolic networks

and their cellular processes exhibit a certain degree of conservation among eukaryotes. These organisms play a crucial role in research with synthetic biology and systems biology as well. However

due to the complexity of their metabolic networks and the variability of cellular activities

study and design of pathways for yeasts still present considerable challenges. To address these issues

researchers have developed genome-scale models

which are mathematical framework that integrates genomic

biochemical

and physiological data to simulate cellular processes and predict the relationship between genotype and phenotype

which are further used to simulate cellular functions and predict cell behaviors under different conditions

providing a systematic approach for understanding and engineering biological systems. This review introduces methods for building and analyzing genome-scale models of yeasts

including traditional metabolic models and their derived multi-constraint and multi-process models. It also traces the development of yeast models over time. Furthermore

this article discusses recent applications of yeast models in areas such as designing yeasts as cell factories for producing valuable compounds

studying microbial physiology

optimizing cultivation conditions

and simulating microbial community interactions. These models also provide insights into identifying potential metabolic engineering targets for optimizing cellular functions. Despite the advantages of the genome-scale models

their develo

pment and application are still limited in several aspects

such as incomplete data on metabolic pathways

limited focus on secondary metabolism

and high barriers to use

particularly for users without programming backgrounds. This review proposes several strategies to address these challenges. To enhance the development of traditional models

it is crucial to incorporate more comprehensive datasets

with a particular emphasis on secondary metabolism and metabolic dark matters. Additionally

improving the accessibility of models requires the development of user-friendly platforms

the provision of clear and standardized tutorials. These strategies can lower barriers for users

and promote applications of the genome-scale models.

关键词

Keywords

references

GOFFEAU A , BARRELL B G , BUSSEY H , et al . Life with 6000 genes [J ] . Science , 1996 , 274 ( 5287 ): 546 - 567 .

BERNARD A , ROSSIGNOL T , PARK Y K . Biotechnological approaches for producing natural pigments in yeasts [J ] . Trends in Biotechnology , 2024 , 42 ( 12 ): 1644 - 1662 .

LIAN J Z , MISHRA S , ZHAO H M . Recent advances in metabolic engineering of Saccharomyces cerevisiae : new tools and their applications [J ] . Metabolic Engineering , 2018 , 50 : 85 - 108 .

ZHANG Y W , YANG J J , QIAN F H , et al . Engineering a xylose fermenting yeast for lignocellulosic ethanol production [J ] . Nature Chemical Biology , 2025 , 21 ( 3 ): 443 - 450 .

IGUCHI H , YURIMOTO H , SAKAI Y . Interactions of methylotrophs with plants and other heterotrophic bacteria [J ] . Microorganisms , 2015 , 3 ( 2 ): 137 - 151 .

HENRIQUES D , MINEBOIS R , DOS SANTOS D , et al . A dynamic genome-scale model identifies metabolic pathways associated with cold tolerance in Saccharomyces kudriavzevii [J ] . Microbiology Spectrum , 2023 , 11 ( 3 ): e03519-22 .

HASSAN Y , CHEW S Y , THAN L T L . Candida glabrata : pathogenicity and resistance mechanisms for adaptation and survival [J ] . Journal of Fungi , 2021 , 7 ( 8 ): 667 .

BERNAUER L , RADKOHL A , LEHMAYER L G K , et al . Komagataella phaffii as emerging model organism in fundamental research [J ] . Frontiers in Microbiology , 2021 , 11 : 607028 .

CAVALLO E , CHARREAU H , CERRUTTI P , et al . Yarrowia lipolytica : a model yeast for citric acid production [J ] . FEMS Yeast Research , 2017 , 17 ( 8 ): fox084 .

MISHRA P , LEE N R , LAKSHMANAN M , et al . Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica [J ] . BMC Systems Biology , 2018 , 12 ( S2 ): 12 .

EDWARDS J S , PALSSON B O . Systems properties of the Haemophilus influenzae Rd metabolic genotype [J ] . Journal of Biological Chemistry , 1999 , 274 ( 25 ): 17410 - 17416 .

KANEHISA M , GOTO S . KEGG: kyoto encyclopedia of genes and genomes [J ] . Nucleic Acids Research , 2000 , 28 ( 1 ): 27 - 30 .

KARP P D , RILEY M , SAIER M , et al . The EcoCyc and MetaCyc databases [J ] . Nucleic Acids Research , 2000 , 28 ( 1 ): 56 - 59 .

SCHAEFER C F , ANTHONY K , KRUPA S , et al . PID: the pathway interaction database [J ] . Nucleic Acids Research , 2009 , 37 ( Database issue ): D674 - D679 .

HEIRENDT L , ARRECKX S , PFAU T , et al . Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0 [J ] . Nature Protocols , 2019 , 14 ( 3 ): 639 - 702 .

EBRAHIM A , LERMAN J A , PALSSON B O , et al . COBRApy: COnstraints-based reconstruction and analysis for python [J ] . BMC Systems Biology , 2013 , 7 : 74 .

WANG H , MARCIŠAUSKAS S , SÁNCHEZ B J , et al . RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor [J ] . PLoS Computational Biology , 2018 , 14 ( 10 ): e1006541 .

BÜCHEL F , RODRIGUEZ N , SWAINSTON N , et al . Path2Models: large-scale generation of computational models from biochemical pathway maps [J ] . BMC Systems Biology , 2013 , 7 : 116 .

MACHADO D , ANDREJEV S , TRAMONTANO M , et al . Fast automated reconstruction of genome-scale metabolic models for microbial species and communities [J ] . Nucleic Acids Research , 2018 , 46 ( 15 ): 7542 - 7553 .

HENRY C S , DEJONGH M , BEST A A , et al . High-throughput generation, optimization and analysis of genome-scale metabolic models [J ] . Nature Biotechnology , 2010 , 28 ( 9 ): 977 - 982 .

ARKIN A P , COTTINGHAM R W , HENRY C S , et al . KBase: the United States department of energy systems biology knowledgebase [J ] . Nature Biotechnology , 2018 , 36 ( 7 ): 566 - 569 .

KUEPFER L , SAUER U , BLANK L M . Metabolic functions of duplicate genes in Saccharomyces cerevisiae [J ] . Genome Research , 2005 , 15 ( 10 ): 1421 - 1430 .

MACKIE A , KESELER I M , NOLAN L , et al . Dead end metabolites: defining the known unknowns of the E . coli metabolic network [J ] . PLoS One , 2013 , 8 ( 9 ): e75210 .

ORTH J D , CONRAD T M , NA J , et al . A comprehensive genome-scale reconstruction of Escherichia coli metabolism: 2011 [J ] . Molecular Systems Biology , 2011 , 7 : 535 .

KING Z A , LU J , DRÄGER A , et al . BiGG Models: a platform for integrating, standardizing and sharing genome-scale models [J ] . Nucleic Acids Research , 2016 , 44 ( D1 ): D515 - D522 .

CHELLIAH V , LAIBE C , NOVÈRE N L . BioModels database: a repository of mathematical models of biological processes [M/OL ] //DUBITZKY W, WOLKENHAUER O, CHO K H, et al. Encyclopedia of systems biology . New York : Springer , 2013 : 134 - 138 . ( 2013-04-25)[2024-12-01] . https://doi.org/10.1007/978-1-62703-450-0_10 https://doi.org/10.1007/978-1-62703-450-0_10 .

ORTH J D , THIELE I , PALSSON B Ø . What is flux balance analysis? [J ] . Nature Biotechnology , 2010 , 28 ( 3 ): 245 - 248 .

MACGILLIVRAY M , KO A , GRUBER E , et al . Robust analysis of fluxes in genome-scale metabolic pathways [J ] . Scientific Reports , 2017 , 7 ( 1 ): 268 .

O'BRIEN E J , MONK J M , PALSSON B O . Using genome-scale models to predict biological capabilities [J ] . Cell , 2015 , 161 ( 5 ): 971 - 987 .

COVERT M W , XIAO N , CHEN T J , et al . Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli [J ] . Bioinformatics , 2008 , 24 ( 18 ): 2044 - 2050 .

SÁNCHEZ B J , ZHANG C , NILSSON A , et al . Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints [J ] . Molecular Systems Biology , 2017 , 13 ( 8 ): 935 .

THIELE I , JAMSHIDI N , FLEMING R M T , et al . Genome-scale reconstruction of Escherichia coli ’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization [J ] . PLoS Computational Biology , 2009 , 5 ( 3 ): e1000312 .

PATIL K R , NIELSEN J . Uncovering transcriptional regulation of metabolism by using metabolic network topology [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2005 , 102 ( 8 ): 2685 - 2689 .

LU H Z , LI F R , SÁNCHEZ B J , et al . A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism [J ] . Nature Communications , 2019 , 10 ( 1 ): 3586 .

CHEN Y , GUSTAFSSON J , TAFUR RANGEL A , et al . Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0 [J ] . Nature Protocols , 2024 , 19 ( 3 ): 629 - 667 .

ADADI R , VOLKMER B , MILO R , et al . Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters [J ] . PLoS Computational Biology , 2012 , 8 ( 7 ): e1002575 .

MAO Z T , ZHAO X , YANG X , et al . ECMpy, a simplified workflow for constructing enzymatic constrained metabolic network model [J ] . Biomolecules , 2022 , 12 ( 1 ): 65 .

YANG X , MAO Z T , ZHAO X , et al . Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models [J ] . Metabolic Engineering , 2021 , 67 : 133 - 144 .

BI X Y , CHENG Y , XU X H , et al . etiBsu1209: a comprehensive multiscale metabolic model for Bacillus subtilis [J ] . Biotechnology and Bioengineering , 2023 , 120 ( 6 ): 1623 - 1639 .

LLOYD C J , EBRAHIM A , YANG L , et al . COBRAme: a computational framework for genome-scale models of metabolism and gene expression [J ] . PLoS Computational Biology , 2018 , 14 ( 7 ): e1006302 .

BULOVIĆ A , FISCHER S , DINH M , et al . Automated generation of bacterial resource allocation models [J ] . Metabolic Engineering , 2019 , 55 : 12 - 22 .

OFTADEH O , SALVY P , MASID M , et al . A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics [J ] . Nature Communications , 2021 , 12 ( 1 ): 4790 .

JESKE L , PLACZEK S , SCHOMBURG I , et al . BRENDA in 2019: a European ELIXIR core data resource [J ] . Nucleic Acids Research , 2019 , 47 ( D1 ): D542 - D549 .

LI F R , YUAN L , LU H Z , et al . Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction [J ] . Nature Catalysis , 2022 , 5 ( 8 ): 662 - 672 .

O'BRIEN E J , LERMAN J A , CHANG R L , et al . Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction [J ] . Molecular Systems Biology , 2013 , 9 : 693 .

FANG X , LLOYD C J , PALSSON B O . Reconstructing organisms in silico : genome-scale models and their emerging applications [J ] . Nature Reviews Microbiology , 2020 , 18 ( 12 ): 731 - 743 .

MAN O , PILPEL Y . Differential translation efficiency of orthologous genes is involved in phenotypic divergence of yeast species [J ] . Nature Genetics , 2007 , 39 ( 3 ): 415 - 421 .

DUARTE N C , BECKER S A , JAMSHIDI N , et al . Global reconstruction of the human metabolic network based on genomic and bibliomic data [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2007 , 104 ( 6 ): 1777 - 1782 .

VANDERWAEREN L , DOK R , VOORDECKERS K , et al . Saccharomyces cerevisiae as a model system for eukaryotic cell biology, from cell cycle control to DNA damage r esponse [J ] . International Journal of Molecular Sciences , 2022 , 23 ( 19 ): 11665 .

FÖRSTER J , FAMILI I , FU P , et al . Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network [J ] . Genome Research , 2003 , 13 ( 2 ): 244 - 253 .

DUARTE N C , HERRGÅRD M J , PALSSON B Ø . Reconstruction and validation of Saccharomyces cerevisiae i ND750, a fully compartmentalized genome-scale metabolic model [J ] . Genome Research , 2004 , 14 ( 7 ): 1298 - 1309 .

NOOKAEW I , JEWETT M C , MEECHAI A , et al . The genome-scale metabolic model i IN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism [J ] . BMC Systems Biology , 2008 , 2 : 71 .

HERRGÅRD M J , SWAINSTON N , DOBSON P , et al . A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology [J ] . Nature Biotechnology , 2008 , 26 ( 10 ): 1155 - 1160 .

ZOMORRODI A R , MARANAS C D . Improving the i MM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data [J ] . BMC Systems Biology , 2010 , 4 : 178 .

HEAVNER B D , SMALLBONE K , BARKER B , et al . Yeast5-an expanded reconstruction of the Saccharomyces cerevisiae metabolic network [J ] . BMC Systems Biology , 2012 , 6 : 55 .

ÖSTERLUND T , NOOKAEW I , BORDEL S , et al . Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling [J ] . BMC Systems Biology , 2013 , 7 : 36 .

ZHANG C Y , SÁNCHEZ B J , LI F R , et al . Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community [J ] . Molecular Systems Biology , 2024 , 20 ( 10 ): 1134 - 1150 .

DOBSON P D , SMALLBONE K , JAMESON D , et al . Further developments towards a genome-scale metabolic model of yeast [J ] . BMC Systems Biology , 2010 , 4 : 145 .

HEAVNER B D , SMALLBONE K , PRICE N D , et al . Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance [J ] . Database , 2013 , 2013 : bat059 .

AUNG H W , HENRY S A , WALKER L P . Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism [J ] . Industrial Biotechnology , 2013 , 9 ( 4 ): 215 - 228 .

HAN S Y , WU K , WANG Y H , et al . Auxotrophy-based curation improves the consensus genome-scale metabolic model of yeast [J ] . Synthetic and Systems Biotechnology , 2024 , 9 ( 4 ): 861 - 870 .

MO M L , PALSSON B Ø , HERRGÅRD M J . Connecting extracellular metabolomic measurements to intracellular flux states in yeast [J ] . BMC Systems Biology , 2009 , 3 : 37 .

TSOUKA S , HATZIMANIKATIS V . redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast [J ] . FEMS Yeast Research , 2020 , 20 ( 2 ): foaa006 .

SCOTT W T JR , SMID E J , NOTEBAART R A , et al . Curation and analysis of a Saccharomyces cerevisiae genome-scale metabolic model for predicting production of sensory impact molecules under enological conditions [J ] . Processes , 2020 , 8 ( 9 ): 1195 .

CHOWDHURY R , CHOWDHURY A , MARANAS C D . Using gene essentiality and synthetic lethality information to correct yeast and CHO cell genome-scale models [J ] . Metabolites , 2015 , 5 ( 4 ): 536 - 570 .

ATA Ö , ERGÜN B G , FICKERS P , et al . What makes Komagataella phaffii non-conventional? [J ] . FEMS Yeast Research , 2021 , 21 ( 8 ): foab059 .

SOHN S B , GRAF A B , KIM T Y , et al . Genome-scale metabolic model of methylotrophic yeast Pichia pastoris and its use for in silico analysis of heterologous protein production [J ] . Biotechnology Journal , 2010 , 5 ( 7 ): 705 - 715 .

CHUNG B K , SELVARASU S , CAMATTARI A , et al . Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement [J ] . Microbial Cell Factories , 2010 , 9 : 50 .

CASPETA L , SHOAIE S , AGREN R , et al . Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials [J ] . BMC Systems Biology , 2012 , 6 : 24 .

TOMÀS-GAMISANS M , FERRER P , ALBIOL J . Integration and validation of the genome-scale metabolic models of Pichia pastoris : a comprehensive update of protein glycosylation pathways, lipid and energy metabolism [J ] . PLoS One , 2016 , 11 ( 1 ): e0148031 .

TOMÀS-GAMISANS M , FERRER P , ALBIOL J . Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources [J ] . Microbial Biotechnology , 2018 , 11 ( 1 ): 224 - 237 .

LOIRA N , DULERMO T , NICAUD J M , et al . A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica [J ] . BMC Systems Biology , 2012 , 6 : 35 .

KERKHOVEN E J , POMRANING K R , BAKER S E , et al . Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica [J ] . NPJ Systems Biology and Applications , 2016 , 2 : 16005 .

GUO Y F , SU L Q , LIU Q , et al . Dissecting carbon metabolism of Yarrowia lipolytica type strain W29 using genome-scale metabolic modelling [J ] . Computational and Structural Biotechnology Journal , 2022 , 20 : 2503 - 2511 .

ZHU Z W , ZHANG S F , LIU H W , et al . A multi-omic map of the lipid-producing yeast Rhodosporidium toruloides [J ] . Nature Communications , 2012 , 3 : 1112 .

TIUKOVA I A , PRIGENT S , NIELSEN J , et al . Genome-scale model of Rhodotorula toruloides metabolism [J ] . Biotechnology and Bioengineering , 2019 , 116 ( 12 ): 3396 - 3408 .

KIM J H , CORADETTI S T , KIM Y M , et al . Multi-omics driven metabolic network reconstruction and analysis of lignocellulosic carbon utilization in Rhodosporidium toruloides [J ] . Frontiers in Bioengineering and Biotechnology , 2021 , 8 : 612832 .

LIEBAL U W , FABRY B A , RAVIKRISHNAN A , et al . Genome-scale model reconstruction of the methylotrophic yeast Ogataea polymorpha [J ] . BMC Biotechnology , 2021 , 21 ( 1 ): 23 .

MISHRA P , PARK G Y , LAKSHMANAN M , et al . Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production [J ] . Biotechnology and Bioengineering , 2016 , 113 ( 9 ): 1993 - 2004 .

XU N , LIU L M , ZOU W , et al . Reconstruction and analysis of the genome-scale metabolic network of Candida glabrata [J ] . Molecular BioSystems , 2013 , 9 ( 2 ): 205 - 216 .

VYAS A , FREITAS A V , RALSTON Z A , et al . Fission yeast Schizosaccharomyces pombe : a unicellular "micromammal" model organism [J ] . Current Protocols , 2021 , 1 ( 6 ): e151 .

SOHN S B , KIM T Y , LEE J H , et al . Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico / in vivo mutant growth [J ] . BMC Systems Biology , 2012 , 6 : 49 .

GRIGAITIS P , GRUNDEL D A J , VAN PELT-KLEINJAN E , et al . A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast [J ] . mSystems , 2022 , 7 ( 4 ): e0042322 .

SPOHNER S C , SCHAUM V , QUITMANN H , et al . Kluyveromyces lactis : an emerging tool in biotechnology [J ] . Journal of Biotechnology , 2016 , 222 : 104 - 116 .

DIAS O , PEREIRA R , GOMBERT A K , et al . i OD907, the first genome-scale metabolic model for the milk yeast Kluyveromyces lactis [J ] . Biotechnology Journal , 2014 , 9 ( 6 ): 776 - 790 .

MARCIŠAUSKAS S , JI B Y , NIELSEN J . Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model [J ] . BMC Bioinformatics , 2019 , 20 ( 1 ): 551 .

PAPINI M , NOOKAEW I , UHLÉN M , et al . Scheffersomyces stipitis : a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae [J ] . Microbial Cell Factories , 2012 , 11 : 136 .

BALAGURUNATHAN B , JONNALAGADDA S , TAN L , et al . Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis [J ] . Microbial Cell Factories , 2012 , 11 : 27 .

HILLIARD M , DAMIANI A , HE Q P , et al . Elucidating redox balance shift in Scheffersomyces stipitis ’ fermentative metabolism using a modified genome-scale metabolic model [J ] . Microbial Cell Factories , 2018 , 17 ( 1 ): 140 .

LI G , HU Y T , ZRIMEC J , et al . Bayesian genome scale modelling identifies thermal determinants of yeast metabolism [J ] . Nature Communications , 2021 , 12 ( 1 ): 190 .

CHEN Y , LI F R , MAO J W , et al . Yeast optimizes metal utilization based on metabolic network and enzyme kinetics [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2021 , 118 ( 12 ): e2020154118 .

ELSEMMAN I E , RODRIGUEZ PRADO A , GRIGAITIS P , et al . Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies [J ] . Nature Communications , 2022 , 13 ( 1 ): 801 .

LI F R , CHEN Y , QI Q , et al . Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints [J ] . Nature Communications , 2022 , 13 ( 1 ): 2969 .

DOMENZAIN I , SÁNCHEZ B , ANTON M , et al . Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0 [J ] . Nature Communications , 2022 , 13 ( 1 ): 3766 .

REĶĒNA A , PINHEIRO M J , BONTURI N , et al . Genome-scale metabolic modeling reveals metabolic trade-offs associated with lipid production in Rhodotorula toruloides [J ] . PLoS Computational Biology , 2023 , 19 ( 4 ): e1011009 .

BATTJES J , GRIGAITIS P , HOVING M , et al . Mitochondrial efficiency determines Crabtree effect across yeasts [EB/OL ] . bioRxiv , 2024 : 2024 .11. 01 .621473. ( 2024-11-03 )[ 2024-11-15 ] . https://doi.org/10.1101/2024.11.01.621473 https://doi.org/10.1101/2024.11.01.621473 .

GRIGAITIS P , VAN DEN BOGAARD S L , TEUSINK B . Elevated energy costs of biomass production in mitochondrial respiration-deficient Saccharomyces cerevisia [J ] . FEMS Yeast Research , 2023 , 23 : foad008 .

CHOI B , TAFUR RANGEL A , KERKHOVEN E J , et al . Engineering of Saccharomyces cerevisiae for enhanced metabolic robustness and L-lactic acid production from lignocellulosic biomass [J ] . Metabolic Engineering , 2024 , 84 : 23 - 33 .

QIN N , LI L Y , WAN X Z , et al . Increased CO 2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast [J ] . Nature Communications , 2024 , 15 ( 1 ): 1591 .

ISHCHUK O P , DOMENZAIN I , SÁNCHEZ B J , et al . Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2022 , 119 ( 30 ): e2108245119 .

BERNAT-CAMPS N , EBNER K , SCHUSTERBAUER V , et al . Enabling growth-decoupled Komagataella phaffii recombinant protein production based on the methanol-free P DH promoter [J ] . Frontiers in Bioengineering and Biotechnology , 2023 , 11 : 1130583 .

WEFELMEIER K , SCHMITZ S , KÖSTERS B J , et al . Methanol bioconversion into C 3 , C 4 , and C 5 platform chemicals by the yeast Ogataea polymorpha [J ] . Microbial Cell Factories , 2024 , 23 ( 1 ): 8 .

DA VEIGA MOREIRA J , JOLICOEUR M , SCHWARTZ L , et al . Fine-tuning mitochondrial activity in Yarrowia lipolytica for citrate overproduction [J ] . Scientific Reports , 2021 , 11 ( 1 ): 878 .

SCOTT W T JR , SMID E J , BLOCK D E , et al . Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts [J ] . Microbial Cell Factories , 2021 , 20 ( 1 ): 204 .

MESQUITA T J B , SARGO C R , FUZER NETO J R , et al . Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations [J ] . Microbial Cell Factories , 2019 , 18 ( 1 ): 150 .

BOOJARI M A , GHALEDARI F R , MOTAMEDIAN E , et al . Developing a metabolic model-based fed-batch feeding strategy for Pichia pastoris fermentation through fine-tuning of the methanol utilization pathway [J ] . Microbial Biotechnology , 2023 , 16 ( 6 ): 1344 - 1359 .

TAMIRES MOREIRA MELO N , PONTES G C , PROCÓPIO D P , et al . Evaluation of product distribution in chemostat and batch fermentation in lactic acid-producing Komagataella phaffii strains utilizing glycerol as substrate [J ] . Microorganisms , 2020 , 8 ( 5 ): 781 .

FERNÁNDEZ-NIÑO M , RODRÍGUEZ-CUBILLOS M J , HERRERA-ROCHA F , et al . Dissecting industrial fermentations of fine flavour cocoa through metagenomic analysis [J ] . Scientific Reports , 2021 , 11 ( 1 ): 8638 .

PONOMAROVA O , GABRIELLI N , SÉVIN D C , et al . Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow [J ] . Cell Systems , 2017 , 5 ( 4 ): 345 - 357.e6 .

ÖSTERLUND T , NOOKAEW I , NIELSEN J . Fifteen years of large scale metabolic modeling of yeast: developments and impacts [J ] . Biotechnology Advances , 2012 , 30 ( 5 ): 979 - 988 .

GU C D , KIM G B , KIM W J , et al . Current status and applications of genome-scale metabolic models [J ] . Genome Biology , 2019 , 20 ( 1 ): 121 .

DOMENZAIN I , LI F R , KERKHOVEN E J , et al . Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species [J ] . FEMS Yeast Research , 2021 , 21 ( 1 ): foab002 .

MAHADEVAN R , LOVLEY D R . The degree of redundancy in metabolic genes is linked to mode of metabolism [J ] . Biophysical Journal , 2008 , 94 ( 4 ): 1216 - 1220 .

DEUTSCHER D , MEILIJSON I , KUPIEC M , et al . Multiple knockout analysis of genetic robustness in the yeast metabolic network [J ] . Nature Genetics , 2006 , 38 ( 9 ): 993 - 998 .

CHEN L F , VITKUP D . Predicting genes for orphan metabolic activities using phylogenetic profiles [J ] . Genome Biology , 2006 , 7 ( 2 ): R17 .

RAZAGHI-MOGHADAM Z , BABADI F S , NIKOLOSKI Z . Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes [J ] . PLoS Computational Biology , 2024 , 20 ( 11 ): e1012576 .

SÁNCHEZ B J , LI F R , KERKHOVEN E J , et al . SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework [J ] . BMC Systems Biology , 2019 , 13 ( 1 ): 4 .

DE MOURA FERREIRA M A , WENDERING P , AREND M , et al . Accurate prediction of in vivo protein abundances by coupling constraint-based modelling and machine learning [J ] . Metabolic Engineering , 2023 , 80 : 184 - 192 .

BURGARD A P , NIKOLAEV E V , SCHILLING C H , et al . Flux coupling analysis of genome-scale metabolic network reconstructions [J ] . Genome Research , 2004 , 14 ( 2 ): 301 - 312 .

SMALLBONE K , SIMEONIDIS E , SWAINSTON N , et al . Towards a genome-scale kinetic model of cellular metabolism [J ] . BMC Systems Biology , 2010 , 4 : 6 .

MOHAMMADIPEYHANI H , HAFNER J , SVESHNIKOVA A , et al . Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx [J ] . Nature Communications , 2022 , 13 ( 1 ): 1560 .

NIELSEN J , KEASLING J D . Engineering cellular metabolism [J ] . Cell , 2016 , 164 ( 6 ): 1185 - 1197 .

CROWE S A , LIU Y Z , ZHAO X X , et al . Advances in engineering nucleotide sugar metabolism for natural product glycosylation in Saccharomyces cerevisiae [J ] . ACS Synthetic Biology , 2024 , 13 ( 6 ): 1589 - 1599 .

LERMA-ORTIZ C , JEFFRYES J G , COOPER A J L , et al . ‘Nothing of chemistry disappears in biology’: the top 30 damage-prone endogenous metabolites [J ] . Biochemical Society Transactions , 2016 , 44 ( 3 ): 961 - 971 .

WU K , LIU H , SUN M , et al . Yeast-MetaTwin for systematically exploring yeast metabolism through retrobiosynthesis and deep learning [EB/OL ] . bioRxiv , 2024 : 2024 .09. 02 .610684. ( 2024-09-02 )[ 2024-11-01 ] . https://doi.org/10.1101/2024.09.02.610684 https://doi.org/10.1101/2024.09.02.610684 .

NIELSEN J C , NIELSEN J . Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism [J ] . Synthetic and Systems Biotechnology , 2017 , 2 ( 1 ): 5 - 12 .

LU H Z , KERKHOVEN E J , NIELSEN J . Multiscale models quantifying yeast physiology: towards a whole-cell model [J ] . Trends in Biotechnology , 2022 , 40 ( 3 ): 291 - 305 .

SEN P , OREŠIČ M . Integrating omics data in genome-scale metabolic modeling: a methodological perspective for precision medicine [J ] . Metabolites , 2023 , 13 ( 7 ): 855 .

GUO J , IBANEZ-LOPEZ A S , GAO H Y , et al . Automated chemical reaction extraction from scientific literature [J ] . Journal of Chemical Information and Modeling , 2022 , 62 ( 9 ): 2035 - 2045 .

BESSELL B , LOECKER J , ZHAO Z Y , et al . COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery [J ] . Briefings in Bioinformatics , 2023 , 24 ( 6 ): bbad387 .

THIELE I , PALSSON B Ø . A protocol for generating a high-quality genome-scale metabolic reconstruction [J ] . Nature Protocols , 2010 , 5 ( 1 ): 93 - 121 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

酵母合成单萜类化合物的研究进展

合成生物学赋能细胞膜纳米颗粒的精准诊疗

酚酸类化合物：从植物提取到生物合成