Current status and applications of genome-scale metabolic models

Gu, Changdai; Kim, Gi Bae; Kim, Won Jun; Kim, Hyun Uk; Lee, Sang Yup

doi:10.1186/s13059-019-1730-3

Cited by 578 publications

(561 citation statements)

References 173 publications

(196 reference statements)

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“…Genome‐scale metabolic models (GEMs) are the most widely used stoichiometry‐based model for studying cell physiologies (Gu, Kim, Kim, Kim, & Lee, ; W. J. Kim, Kim, & Lee, ). By setting up an appropriate objective function (sometimes also a subobjective function) and flux constraints, the GEMs can be used to predict the cell flux distribution, the pathway variability (Mahadevan & Schilling, ) and further be used to seek for a potentially beneficial genome modification (Burgard, Pharkya, & Maranas, ; Thiele & Palsson, ).…”

Section: Quantitative Metabolomics and Its Application In Systems Metmentioning

confidence: 99%

“…GSM has already been accepted as the major modeling approach for metabolic studies at the systems level. Up till now, there are GSMs available for more than 6,239 organisms and 183 of them have a GEM reconstruction and curation manually done (Gu et al, ). Besides the increase in quantity, more details and constraints regarding GSM construction and application are taken into account.…”

Section: Future Prospectmentioning

confidence: 99%

“…In general, these models can be categorized into two main groups: the stoichiometry-based model and the kinetic-based model. Genome-scale metabolic models (GEMs) are the most widely used stoichiometry-based model for studying cell physiologies (Gu, Kim, Kim, Kim, & Lee, 2019; W. J. Kim, Kim, & Lee, 2017).…”

Section: Integration Of Metabolomics Data Into Metabolic Networkmentioning

confidence: 99%

Section: Future Prospectmentioning

confidence: 99%

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Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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Section: Quantitative Metabolomics and Its Application In Systems Metmentioning

confidence: 99%

Section: Future Prospectmentioning

confidence: 99%

Section: Integration Of Metabolomics Data Into Metabolic Networkmentioning

confidence: 99%

Section: Future Prospectmentioning

confidence: 99%

See 2 more Smart Citations

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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“…To interpret the impact on function, mathematical models of pathways can be used. For example, genome-scale metabolic network reconstructions are knowledgebases of all metabolic pathways in an organism [3][4][5] . These networks directly link genotype to phenotype, since they mathematically describe the mechanisms by which all cell parts (e.g., membranes, proteins, etc.)…”

Section: Introductionmentioning

confidence: 99%

What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

Richelle

Kellman²,

Wenzel³

et al. 2020

Preprint

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Large-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

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Integrating Metabolic Modeling and High‐Throughput Data to Characterize Diatoms Metabolism

Tibocha‐Bonilla

Kumar

Zengler

et al. 2023

The Mathematical Biology of Diatoms

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Current status and applications of genome-scale metabolic models

Cited by 578 publications

References 173 publications

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

Integrating Metabolic Modeling and High‐Throughput Data to Characterize Diatoms Metabolism

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