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To reduce the computational cost of FBA-based screening, we constructed simplified metabolic models based on our previous study (Ohno et al., 2013), which provides identical flux estimations and screening results to the original model.
This work has important implications for the fields of probiogenomics and metagenomics, and functional studies of reference genomes and the mammalian microbiome will require deeper insights into metabolic models based on patterns of gene expression.
With the increasing availability of Brachyspira genome sequences, such technology should facilitate the validation of metabolic models based on genome sequence.
Here we present a novel workflow, FASTCORMICS, for the fast, robust and accurate generation of metabolic models based on transcriptomics data generated by microarrays and use FASTCORMICS to generate multiple metabolic models across tens of primary cell types.
To date, transcriptomic data from public repositories have explored a much broader range of human tissue/cell types and pathophysiological conditions: we were able to use mCADRE to build 126 human tissue-specific metabolic models based on transcriptomic data from a single microarray platform.
The constraints-based approach, in particular flux balance analysis, has been shown to be highly predictive of growth phenotypes in many microbial systems [ 8, 9] and can be used to construct large scale metabolic models based on genome sequences in the absence of kinetic information, making it particularly attractive for less well-studied organisms like S. coelicolor.
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In this paper, we upgraded P. pastoris genome-scale metabolic model based on the combination of latest genome annotations and literatures.
This review presents the perspective that in silico metabolic modelling based on genome-scale metabolic networks can be used for understanding the metabolisms of the anodic microorganisms and optimizes the performance of their metabolic networks for MFCs.
The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production.
The SuBliMinaL Toolbox [ 33] provides a very interesting alternative for metabolic modeling, based on KEGG data.
A metabolic model based on these reactions was comprehensively validated with experimental data [ 10].
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metabolic systems based
metabolic models used
metabolic modelling based
metabolic fluxes based
metabolic networks based
metabolic reconstructions based
metabolic differences based
metabolic studies based
metabolic models described
metabolic pathways based
metabolic signals based
metabolic events based
metabolic processes based
metabolic model based
distribution models based
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