BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Norsigian, Charles J.; Pusarla, Neha; McConn, John Luke; Yurkovich, James T.; Dräger, Andreas; Kim, Jaehyung; King, Zachary A.

doi:10.1093/nar/gkz1054

Cited by 175 publications

(227 citation statements)

References 34 publications

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“…Our analysis of visualizations of the nucleotide sequences of various species of living organisms confirms that the nucleotide composition can be identical in organisms that are not related in the phylogenetic tree and different in related organisms [13][14]. It should be noted that visualization algorithms are implemented in spaces of binary-orthogonal functions.…”

Section: Resultssupporting

confidence: 61%

Scaling and Visualization of Nucleotide Sequences

Stepanyan

Khussein

2019

EPJ Web Conf.

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Algorithms for scaling and visualization of nucleotide sequences developed in this study allow identifying relationships between the biochemical parameters of DNA and RNA molecules with scale invariance, fractal clusters, nonlinear ordering and symmetry and noise immunity of visual representations in orthogonal coordinate systems. The algorithms are capable of displaying structures of the nucleotide sequences of living organisms by visualizing them in spaces of various dimensions and scales. Approximately one hundred genes (protozoa, plants, fungi, animals, viruses) were analysed and examples of visualization of the nucleotide composition of genomes of various species have been presented. The developed method contributes to an in-depth understanding of the principles of genetic coding and simplifying the perception of genetic information due to the algorithmic interpretation of the basic properties of polynucleotide fragments with visualization of the final geometric structure of the genetic code.

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Section: Resultssupporting

confidence: 61%

Scaling and Visualization of Nucleotide Sequences

Stepanyan

Khussein

2019

EPJ Web Conf.

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“…All reactions, metabolites stoichiometry and identificators were extracted from the BiGG Models database. 59 The resulting model was validated for mass balances and metabolite compartment formulas with COBRApy validation methods. 60 Once properly balanced, a growth model was created and analyzed.…”

Section: Metabolic Modelingmentioning

confidence: 99%

Dynamic control over feedback regulatory mechanisms improves NADPH fluxes and xylitol biosynthesis in engineeredE. coli

Moreb

et al. 2020

Preprint

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We report improved NADPH flux and xylitol biosynthesis in engineered E. coli. Xylitol is produced from xylose via an NADPH dependent reductase. We utilize two-stage dynamic metabolic control to compare two approaches to optimize xylitol biosynthesis, a stoichiometric approach, wherein competitive fluxes are decreased, and a regulatory approach wherein the levels of key regulatory metabolites are reduced. The stoichiometric and regulatory approaches lead to a 16 fold and 100 fold improvement in xylitol production, respectively. Strains with reduced levels of enoyl-ACP reductase and glucose-6-phosphate dehydrogenase, led to altered metabolite pools resulting in the activation of the membrane bound transhydrogenase and a new NADPH generation pathway, namely pyruvate ferredoxin oxidoreductase coupled with NADPH dependent ferredoxin reductase, leading to increased NADPH fluxes, despite a reduction in NADPH pools. These strains produced titers of 200 g/L of xylitol from xylose at 86% of theoretical yield in instrumented bioreactors. We expect dynamic control over enoyl-ACP reductase and glucose-6-phosphate dehydrogenase to broadly enable improved NADPH dependent bioconversions.HighlightsDecreases in NADPH pools lead to increased NADPH fluxesPyruvate ferredoxin oxidoreductase coupled with NADPH-ferredoxin reductase improves NADPH production in vivo.Dynamic reduction in acyl-ACP/CoA pools alleviate inhibition of membrane bound transhydrogenase and improve NADPH fluxXylitol titers > 200g/L in fed batch fermentations with xylose as a sole feedstock.

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“…The primary limitation of MDPbiomeGEM is the availability (and accuracy) of GEMs for the selected microbes of interest. The metabolic networks for commonly-studied microbes, such as those in the human gut, have often been constructed as a GEM, however bacteria from the soil microbiome are scarce in the global GEM databases such as BiGG [86]. In addition, these metabolic networks often require additional case-specific fine-tuning, as was done with the GEMs provided by [65], which in turn requires real-world data and/or metabolic knowledge that may or may not be available.…”

Section: Discussionmentioning

confidence: 99%

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

García-Jiménez

Carrasco

Medina

et al. 2020

Preprint

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Background : There are few large longitudinal microbiome studies, and fewer that include controlled, well-annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Results : Our novel computational system simulates the dynamics of microbial communities under perturbations using genome-scale metabolic models (GEMs). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; and/or b) the microorganisms present in the community to model, for example, probiotics or pathogen infection. These simulations generate the quantity and types of information required by MDPbiome, an AI system which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We call this novel combination of technologies "MDPbiomeGEM"'. We demonstrate, in a Crohn’s disease microbiome, that MDPbiomeGEM correctly models the influence of both prebiotic fiber and a probiotic, resulting in a recommendation to consume inulin to recover from dysbiosis, consistent with prior biomedical knowledge. When used to model the soil microbiome's ability to degrade the herbicide Atrazine, differing recommendations arise depending on the highly variable state of the initial soil microbial composition, highlighting the relevance of both phosphate and microbes (i.e. Halobacillus sp. and H.stevensii ) in a directed microbiome engineering strategy, consistent with previously published observations. Conclusions : MDPbiomeGEM generates large volumes of longitudinal data of complex microbial communities experiencing perturbations. Machine learning on these data reveal patterns consistent with existing biological knowledge, supporting the validity of the approach. MDPbiomeGEM could save research resources by optimizing sample collection in metagenomics studies through identification of "informative" scenarios/time-points, or by predicting optimal in-vitro culture formulations for generating performant synthetic microbial communities. Finally, MDPbiomeGEM outputs include detailed information about the metabolic state of the community, which can be used to further interpret the impact of perturbations, and potentially could be used to predict novel metabolic biomarkers of a microbiome's state.

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BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Cited by 175 publications

References 34 publications

Scaling and Visualization of Nucleotide Sequences

Scaling and Visualization of Nucleotide Sequences

Dynamic control over feedback regulatory mechanisms improves NADPH fluxes and xylitol biosynthesis in engineeredE. coli

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

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