Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Rosmalen, Rik P. van; Smith, Robert W.; Santos, Vítor A. P. Martins dos; Fleck, Christian; Suárez-Diez, María

doi:10.1016/j.ymben.2021.01.008

Cited by 30 publications

(17 citation statements)

References 59 publications

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“…This model constitutes a more ‘computationally’ efficient metabolic network compared to the large-scale iDG1237. This network could be applied for exploring metabolic phenotypes using constraint-based methods that require high computational power, such as flux sampling [ 68 ], thermodynamically constrained stoichiometric models that use energy balance as a constraint [ 69 ], or large-scale kinetic modeling [ 32 , 70 ].…”

Section: Resultsmentioning

confidence: 99%

TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus

et al. 2021

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Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of β-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis using shadow prices provided a first view of the metabolites positive and negatively associated with the scenarios of low and high CA production.

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Section: Resultsmentioning

confidence: 99%

TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus

et al. 2021

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“…Parameter estimation and model fitting is another major challenge in effectively utilizing GEMs. Constraint based GEMs which are based on linear programming do not include any time dimensions and do not account of metabolite concentrations [189]. Dynamic/kinetic constraint-based GEMs apply enzyme kinetics to increase the scope of these models.…”

Section: Challenges Associated With Reconstruction Of Gem and Omics D...mentioning

confidence: 99%

“…Due to this, kinetic models are still not as accepted as constraint based GEMs [190]. However, there are some efforts to make kinetic models more acceptable to the modeling community by creating sub-networks of a bigger model where kinetic parameters can be fitted easily [189].…”

Section: Challenges Associated With Reconstruction Of Gem and Omics D...mentioning

confidence: 99%

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

et al. 2021

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Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

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“…Although such reconstructions are continuously improved, structural uncertainty remains, for example due to enzymes of unknown function, enzyme promiscuity, or unclear subcellular localization of enzymes. Automated model reduction algorithms for generating targeted kinetic models from genome-scale metabolic reconstructions have been devised that can significantly speed-up model development [26,27]. However, care has to be taken, that the (over-)reduced model complexity does not skew analysis results Hameri et al [28].…”

Section: Constructing Kinetic Modelsmentioning

confidence: 99%

Dynamical models for metabolomics data integration

Lakrisenko,

Weindl

2021

Preprint

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As metabolomics datasets are becoming larger and more complex, there is an increasing need for modelbased data integration and analysis to optimally leverage these data. Dynamical models of metabolism allow for the integration of heterogeneous data and the analysis of dynamical phenotypes. Here, we review recent efforts in using dynamical metabolic models for data integration, focusing on approaches that are not restricted to steady-state measurements or that require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and that, although challenges remain, dynamical modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential.

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Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Cited by 30 publications

References 59 publications

TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus

TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Dynamical models for metabolomics data integration

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