Comprehensive understanding of <i>Saccharomyces cerevisiae</i> phenotypes with whole‐cell model WM_S288C

Ye, Chao; Xu, Nan; Gao, Cong; Liu, Gao-Qiang; Xu, Jianzhong; Zhang, Weiguo; Chen, Xiulai; Nielsen, Jens; Li, Liming

doi:10.1002/bit.27298

Cited by 27 publications

(24 citation statements)

References 53 publications

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“…Similar models to yETFL developed for S. cerevisiae are GECKO models, ecYeast7 10 and ecYeast8 22 , and WM_S288C 23 , a whole-cell model. The Gecko models contain phenomenological constraints for proteome limitations.…”

Section: Resultsmentioning

confidence: 99%

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

et al. 2021

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Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.

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Section: Resultsmentioning

confidence: 99%

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

et al. 2021

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“…To address this problem, manual efforts are currently needed to filter out less relevant targets and add intuitively promising ones based on existing knowledge. In addition, applying our approach to new models that enhance GSMs with more levels of information, such as kinetics 63 , gene expression 64 , and regulation 65 is envisioned to further improve the model's predictive power.…”

Section: Discussionmentioning

confidence: 99%

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

et al. 2020

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Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

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“…Multiscale models have been developed for three different microbial organisms, Mycoplasma genitalium [36], E. coli [37][38][39], and S. cerevisiae [40,41].…”

Section: Learning From Multiscale Microbial Modelsmentioning

confidence: 99%

“…In S. cerevisiae, Ye et al followed the whole-cell modeling principles used to model M. genitalium [36,42] to develop a whole-cell multiscale model. This model bridged the gap between genotype and phenotype predictions by developing and integrating models on one second timescales of 26 cellular processes that span five areas of cell biology [40]. Using this model, they were able to study the dynamic allocation of resources during the cell cycle in real-time simulations.…”

Section: Learning From Multiscale Microbial Modelsmentioning

confidence: 99%

Multiscale plant modeling: from genome to phenome and beyond

Matthews

Marshall‐Colón

2021

Emerging Topics in Life Sciences

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Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

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Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole‐cell model WM_S288C

Cited by 27 publications

References 53 publications

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Multiscale plant modeling: from genome to phenome and beyond

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