Evidence map›Paper›PMID 33836000›Full record

ArticlePLoS computational biology2021

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism.

Linnea Österberg, Iván Domenzain, Julia Münch, Jens Nielsen, Stefan Hohmann, Marija Cvijovic

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Systems biology of yeast metabolism.FEMS yeast research · 2026
    Review
  3. Evaluating plant growth-defense trade-offs by modeling the interaction between primary and secondary metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Machine learning for metabolic pathway optimization: A review.Computational and structural biotechnology journal · 2023
    Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. D-Xylose Sensing inInternational journal of molecular sciences · 2021
    Review
  16. Review
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Linnea ÖsterbergDepartment of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0002-9816-6476
Iván DomenzainDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-5322-2040
Julia MünchDepartment of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0002-9588-2274
Jens NielsenDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0002-9955-6003
Stefan HohmannDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Marija CvijovicDepartment of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.ORCID 0000-0002-5142-5100
Chalmers University of Technology · SEBioInnovation Institute · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer, and neurological disorders. Therefore, unraveling the role of nutrients as signaling molecules and metabolites together with their interconnectivity may provide a deeper understanding of how these conditions occur. Both signaling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition, current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in yeast cells, we developed a hybrid model, combining a Boolean module, describing the main pathways of glucose and nitrogen signaling, and an enzyme-constrained model accounting for the central carbon metabolism of Saccharomyces cerevisiae, using a regulatory network as a link. The resulting hybrid model was able to capture a diverse utalization of isoenzymes and to our knowledge outperforms constraint-based models in the prediction of individual enzymes for both respiratory and mixed metabolism. The model showed that during fermentation, enzyme utilization has a major contribution in governing protein allocation, while in low glucose conditions robustness and control are prioritized. In addition, the model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression, as well as regulatory effects associated with lifespan increase during caloric restriction. Overall, we show that our hybrid model provides a comprehensive framework for the study of the non-trivial effects of the interplay between signaling and metabolism, suggesting connections between the Snf1 signaling pathways and processes that have been related to chronological lifespan of yeast cells.

Indexed as

Signal TransductionGlucoseHumansNitrogenSaccharomyces cerevisiaeGlucoseNitrogen

Identifiers

PMID33836000
PMCPMC8059808
OpenAlexW3152014472

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.