Kinetic modeling of metabolism: Present and future

Islam, Mohammad Mazharul; Schroeder, Wheaton L.; Saha, Rajib

doi:10.1016/j.coisb.2021.04.003

Cited by 17 publications

(12 citation statements)

References 57 publications

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“…Deep learning-predicted k cat values for the metabolic parts of those models can therefore improve their quality and performance, although other challenging kinetic parameters, for example, ribosomal catalytic rates, to be determined in those model formulations cannot be obtained from DLKcat. In addition, model formulations that particularly focus on describing enzyme kinetics 56 could benefit from deep learning-predicted k cat values, so that our DLKcat approach can find a broad application in the modelling field.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

et al. 2022

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Enzyme turnover numbers (kcat) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput kcat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture kcat changes for mutated enzymes and identify amino acid residues with a strong impact on kcat values. We applied this approach to predict genome-scale kcat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted kcat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

et al. 2022

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“…This indicates a need for developing algorithms that leverage gene knockout data in addition to gene expression data for extracting accurate context-specific models. Better model extraction algorithms that can accurately capture the biological state of the cell will simplify the model reduction step commonly performed before computationally intensive analyses such as 13C-MFA (Sacco and Young, 2021), kinetic modeling (Islam et al, 2021), hybrid models(Khaleghi et al, 2021), and models integrating other cell processes with metabolism, such as signaling pathways, protein secretion, and many other processes (Elsemman et al, 2022; Gutierrez et al, 2020; Karr et al, 2012). This will expand the coverage of biological data that can be integrated with metabolic models to gain novel insights into the biology of the organism, study the progression of diseases, identify novel therapeutics, and inform metabolic engineering strategies in production hosts.…”

Section: Discussionmentioning

confidence: 99%

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Gopalakrishnan

Joshi

Gomez

et al. 2022

Preprint

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Genome-scale metabolic models comprehensively describe an organism’s metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism’s phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME inE. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models inE. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

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“…The biochemical and biophysical aspects of these interactions are captured as rate laws for responses and transitions by the physiology boundary in VCell. As a result, mass action and Michaelis-Menten rate laws are available, as are discretionary user-defined generic dynamic expressions [9] . Membrane transport kinetics can be estimated using molecular transition expressions or the electric current for ions.…”

Section: Modelling Platform: the Virtual Cell (Vcell)mentioning

confidence: 99%

Methodology for development of single cell dendritic spine (SCDS) synaptic tagging and capture model using Virtual Cell (VCell)

2023

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Kinetic modeling of metabolism: Present and future

Cited by 17 publications

References 57 publications

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Methodology for development of single cell dendritic spine (SCDS) synaptic tagging and capture model using Virtual Cell (VCell)

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