Dynamic genome-scale modeling of<i>Saccharomyces cerevisiae</i>unravels mechanisms for ester formation during alcoholic fermentation

Scott, William C.; Henriques, David; Smid, Eddy J.; Notebaart, Richard A.; Balsa‐Canto, Eva

doi:10.1101/2022.05.27.493771

Cited by 3 publications

(3 citation statements)

References 80 publications

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“…In those cases, FBA favors growth over production, and cross-feeding metabolites might be overlooked (Fig 3B). Objective functions can be improved by using experimental data, or frameworks can be refined in dynamic tools to use a bi-level objective routine [94] or a multiphase, multiobjective approach [95]. Yet, these amendments may still not apply to all growth scenarios [96].…”

Section: Plos Computational Biologymentioning

confidence: 99%

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Scott,

Benito-Vaquerizo,

Zimmermann

et al. 2023

PLoS Comput Biol

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Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

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Section: Plos Computational Biologymentioning

confidence: 99%

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

Scott,

Benito-Vaquerizo,

Zimmermann

et al. 2023

PLoS Comput Biol

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“…A recent contribution by Henriques et al [2021] proposed a multi-phase, multi-objective dynamic FBA implementation that successfully explained secondary metabolism and brought novel insights into how cold-tolerant yeast species achieve redox balance. The model has been applied to explain the phenotypic differences of various yeast species in batch fermentation [Scott et al, 2023, Henriques et al, 2023].…”

Section: Introductionmentioning

confidence: 99%

A continuous dynamic genome-scale model explains batch fermentations led by species of theSaccharomycesgenus

Moimenta,

Troitiño-Jordedo,

Henriques

et al. 2024

Preprint

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Batch fermentation is a biotechnological dynamic process that produces various products by employing microorganisms that undergo different growth phases: lag, exponential, growth-non-growth, stationary, and decay. Genome-scale constrained-based models are commonly used to explore the phenotypic potential of these microorganisms. Previous studies have primarily used dynamic Flux Balance Analysis (dFBA) to elucidate the metabolism during the exponential phase. However, this approach falls short in addressing the multi-phase nature of the process and secondary metabolism, posing significant challenges in our understanding of batch fermentation. A recent attempt at a solution was a discontinuous, multi-phase, multi-objective dFBA implementation. However, this approximation lacks the mechanistic connection between phases, limiting its applicability in predicting intracellular fluxes during batch fermentation. To overcome these limitations, we combined a novel continuous model with a genome-scale model to predict the distribution of intracellular fluxes throughout the batch fermentation process. The proposed model includes empirical descriptions of regulation that automatically identify the transition between phases. Its application to explain primary and secondary metabolism of Saccharomyces species in batch fermentation results in biological insights that are in good agreement with the previous literature. The ability to account for all process phases and explain secondary metabolism makes this model a valuable and easy-to-use tool for exploring novel fermentation processes.

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“…For instance, leveraging the enforced objective flux (FSEOF) algorithm 17 , Yeast8 and ecYeast8 has been used to obtain a 70-fold improvement in heme production 18 . Kinetic models deprived from yeast-GEM possess the capability to discern species-specific details employing the kinetic parameterization procedure [19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

Zhang,

Sánchez,

et al. 2023

Preprint

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Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM ofSaccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This have increased the quality and scope of this model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or normal conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through the central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference in nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains’ growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine-learning models. Based on those findings we anticipate that Yeast9 will empower systems biology studies of yeast metabolism.

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Dynamic genome-scale modeling ofSaccharomyces cerevisiaeunravels mechanisms for ester formation during alcoholic fermentation

Cited by 3 publications

References 80 publications

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia

A continuous dynamic genome-scale model explains batch fermentations led by species of theSaccharomycesgenus

Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

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