ArticleNPJ systems biology and applications2020
Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.
Article in NPJ systems biology and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 54 citations in OpenAlex.
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- Investigating the Role of β-Disodium Glycerophosphate and Urea in Promoting Growth ofFoods (Basel, Switzerland) · 2024Article
- Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.Briefings in bioinformatics · 2023Review
- Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites.Microorganisms · 2023Review
- Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding.PLoS computational biology · 2023Article
- Stable isotope-assisted metabolite profiling reveals new insights into L-tryptophan chemotrophic metabolism of Rubrivivax benzoatilyticus.World journal of microbiology & biotechnology · 2023Article
- Multi-scale models of whole cells: progress and challenges.Frontiers in cell and developmental biology · 2023Review
- Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases.Frontiers in cardiovascular medicine · 2022Review
- Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.mSystems · 2021Article
- Path to improving the life cycle and quality of genome-scale models of metabolism.Cell systems · 2021Review
- Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes.Annual review of biomedical engineering · 2021Review
- Mechanistic models of microbial community metabolism.Molecular omics · 2021Review
- Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils.Environmental microbiome · 2021Article
- Engineering Host Microbiome for Crop Improvement and Sustainable Agriculture.Frontiers in microbiology · 2021Review
- Strategies for EnhancingFrontiers in bioengineering and biotechnology · 2021Article
- Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-ResistantMicroorganisms · 2020Article
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
- Adaptation of Carbon Source Utilization Patterns ofFrontiers in microbiology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.