Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications

Dahal, Sanjeev; Zhao, Jiao; Yang, Laurence T.

doi:10.1007/s12257-020-0061-2

Cited by 11 publications

(10 citation statements)

References 99 publications

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“…Reactions catalysed by macromolecules are dependent on the synthesis of the macromolecule for the reactions to proceed. ME-models have greater accuracy than M−models as the flux solutions obtained are parsimonious [124] . COBRAme is a software framework built on COBRApy that can be used for building and simulating ME-models [125] .…”

Section: Modelling Microbial Communitiesmentioning

confidence: 99%

“…ME-models can be expanded with such gene regulation data. Protein allocation may also be necessary for identifying the metabolic capabilities of microbial communities, and this gap can be filled by ME-models [124] . Recent progress has enabled the use of microbial community ME-models to study co-cultures growing in an adaptive laboratory evolution (ALE)-optimised experiment [126] .…”

Section: Modelling Microbial Communitiesmentioning

confidence: 99%

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Modelling microbial communities: Harnessing consortia for biotechnological applications

Ibrahim

Raajaraam

Raman

2021

Computational and Structural Biotechnology Journal

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Section: Modelling Microbial Communitiesmentioning

confidence: 99%

Section: Modelling Microbial Communitiesmentioning

confidence: 99%

Modelling microbial communities: Harnessing consortia for biotechnological applications

Ibrahim

Raajaraam

Raman

2021

Computational and Structural Biotechnology Journal

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“…Examples include the cytoplasmic density of macromolecules (Vazquez et al 2008;Goelzer et al 2015), total proteome allocation (Basan et al 2015), and membrane area availability (Szenk, Dill, and de Graff 2017). Thus, a whole-cell understanding of cellular trade-offs between multiple constraints must take into account gene expression alongside metabolic maps (Goelzer and Fromion 2017;Yang et al 2018;Dahal, Zhao, and Yang 2020). Resource allocation constraints have been successfully introduced into genome-wide metabolic models of several organisms as more high-quality expression data has become available (O'Brien et al 2013;Sánchez et al 2017;Y.…”

Section: Introductionmentioning

confidence: 99%

Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

Kleijn

Martínez-Segura

Bertaux

et al. 2021

Preprint

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Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.

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“…Escherichia coli strains have been widely used as a host for developing the cell factory to produce chemicals from sustainable biomass [5][6][7][8][9][10]. One reason is the rich and well-defined information on genetics, physiology, and tools, for metabolic rewiring by engineering [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Effect of deregulation of repressor-specific carbon catabolite repression on carbon source consumption in Escherichia coli

Seong

Jang

2021

Appl Biol Chem

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Escherichia coli has been used as a host to construct the cell factory for biobased production of chemicals from renewable feedstocks. Because galactose is found in marine biomass as a major component, the strategy for galactose utilization in E. coli has been gained more attention. Although galactose and glucose co-fermentation has been reported using the engineered E. coli strain, few reports have covered fermentation supplemented with galactose as a sole carbon source in the mutant lacking the repressor-specific carbon catabolite repression (CCR). Here, we report the effects of the deregulation of the repressor-specific CCR (galR− and galS−) in fermentation supplemented with galactose as a sole carbon source, using the engineered E. coli strains. In the fermentation using the galR− and galS− double mutant (GR2 strain), an increase of rates in sugar consumption and cell growth was observed compared to the parent strain. In the glucose fermentation, wild-type W3110 and its mutant GR2 and GR2PZ (galR−, galS−, pfkA−, and zwf−) consumed sugar at a higher rate than those values obtained from galactose fermentation. However, the GR2P strain (galR−, galS−, and pfkA−) showed no difference between fermentations using glucose and galactose as a sole carbon source. This study provides essential information for galactose fermentation using the CCR-deregulated E. coli strains.

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Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications

Cited by 11 publications

References 99 publications

Modelling microbial communities: Harnessing consortia for biotechnological applications

Modelling microbial communities: Harnessing consortia for biotechnological applications

Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

Effect of deregulation of repressor-specific carbon catabolite repression on carbon source consumption in Escherichia coli

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