IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0276112.html
   My bibliography  Save this article

The choice of the objective function in flux balance analysis is crucial for predicting replicative lifespans in yeast

Author

Listed:
  • Barbara Schnitzer
  • Linnea Österberg
  • Marija Cvijovic

Abstract

Flux balance analysis (FBA) is a powerful tool to study genome-scale models of the cellular metabolism, based on finding the optimal flux distributions over the network. While the objective function is crucial for the outcome, its choice, even though motivated by evolutionary arguments, has not been directly connected to related measures. Here, we used an available multi-scale mathematical model of yeast replicative ageing, integrating cellular metabolism, nutrient sensing and damage accumulation, to systematically test the effect of commonly used objective functions on features of replicative ageing in budding yeast, such as the number of cell divisions and the corresponding time between divisions. The simulations confirmed that assuming maximal growth is essential for reaching realistic lifespans. The usage of the parsimonious solution or the additional maximisation of a growth-independent energy cost can improve lifespan predictions, explained by either increased respiratory activity using resources otherwise allocated to cellular growth or by enhancing antioxidative activity, specifically in early life. Our work provides a new perspective on choosing the objective function in FBA by connecting it to replicative ageing.

Suggested Citation

  • Barbara Schnitzer & Linnea Österberg & Marija Cvijovic, 2022. "The choice of the objective function in flux balance analysis is crucial for predicting replicative lifespans in yeast," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-15, October.
    • Handle: RePEc:plo:pone00:0276112
    DOI: 10.1371/journal.pone.0276112
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0276112
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0276112&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0276112?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hongzhong Lu & Feiran Li & Benjamín J. Sánchez & Zhengming Zhu & Gang Li & Iván Domenzain & Simonas Marcišauskas & Petre Mihail Anton & Dimitra Lappa & Christian Lieven & Moritz Emanuel Beber & Nikola, 2019. "A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giorgio Jansen & Tanda Qi & Vito Latora & Grigoris D. Amoutzias & Daniela Delneri & Stephen G. Oliver & Giuseppe Nicosia, 2024. "Minimisation of metabolic networks defines a new functional class of genes," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Marius Arend & David Zimmer & Rudan Xu & Frederik Sommer & Timo Mühlhaus & Zoran Nikoloski, 2023. "Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Yameng Xu & Xinglong Wang & Chenyang Zhang & Xuan Zhou & Xianhao Xu & Luyao Han & Xueqin Lv & Yanfeng Liu & Song Liu & Jianghua Li & Guocheng Du & Jian Chen & Rodrigo Ledesma-Amaro & Long Liu, 2022. "De novo biosynthesis of rubusoside and rebaudiosides in engineered yeasts," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Feiran Li & Yu Chen & Qi Qi & Yanyan Wang & Le Yuan & Mingtao Huang & Ibrahim E. Elsemman & Amir Feizi & Eduard J. Kerkhoven & Jens Nielsen, 2022. "Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022. "Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Guangjian Li & Hui Liang & Ruichen Gao & Ling Qin & Pei Xu & Mingtao Huang & Min-Hua Zong & Yufei Cao & Wen-Yong Lou, 2024. "Yeast metabolism adaptation for efficient terpenoids synthesis via isopentenol utilization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    7. Gi Bae Kim & Ha Rim Kim & Sang Yup Lee, 2025. "Comprehensive evaluation of the capacities of microbial cell factories," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    8. Mohammad H. Mirhakkak & Xiuqiang Chen & Yueqiong Ni & Thorsten Heinekamp & Tongta Sae-Ong & Lin-Lin Xu & Oliver Kurzai & Amelia E. Barber & Axel A. Brakhage & Sebastien Boutin & Sascha Schäuble & Gian, 2023. "Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    9. Ning Qin & Lingyun Li & Xiaozhen Wan & Xu Ji & Yu Chen & Chaokun Li & Ping Liu & Yijie Zhang & Weijie Yang & Junfeng Jiang & Jianye Xia & Shuobo Shi & Tianwei Tan & Jens Nielsen & Yun Chen & Zihe Liu, 2024. "Increased CO2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    10. Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0276112. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.