ArticleBiotechnology and bioengineering2008
Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis.
Article in Biotechnology and bioengineering, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 170 citations in OpenAlex.
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- A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon onMetabolites · 2023Article
- Physiological limitations and opportunities in microbial metabolic engineering.Nature reviews. Microbiology · 2022Review
- Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.mSystems · 2021Article
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- Electron availability in COMicrobial biotechnology · 2020Article
- Towards continuous industrial bioprocessing with solventogenic and acetogenic clostridia: challenges, progress and perspectives.Journal of industrial microbiology & biotechnology · 2020Review
- Development of a Genome-Scale Metabolic Model ofFrontiers in bioengineering and biotechnology · 2020Article
- Pathway dissection, regulation, engineering and application: lessons learned from biobutanol production by solventogenic clostridia.Biotechnology for biofuels · 2020Review
- Gsmodutils: a python based framework for test-driven genome scale metabolic model development.Bioinformatics (Oxford, England) · 2019Article
- Kinetic modeling of Stickland reactions-coupled methanogenesis for a methanogenic culture.AMB Express · 2019Article
- Metabolism the Difficile Way: The Key to the Success of the PathogenFrontiers in microbiology · 2019Review
- Development of aPeerJ · 2018Article
- Bio-production of gaseous alkenes: ethylene, isoprene, isobutene.Biotechnology for biofuels · 2018Review
- Amino acid catabolism-directed biofuel production inBiotechnology reports (Amsterdam, Netherlands) · 2017Review
- Clostridium butyricum maximizes growth while minimizing enzyme usage and ATP production: metabolic flux distribution of a strain cultured in glycerol.BMC systems biology · 2017Article
- Mathematical modelling of clostridial acetone-butanol-ethanol fermentation.Applied microbiology and biotechnology · 2017Review
- Model-based quantification of metabolic interactions from dynamic microbial-community data.PloS one · 2017Article
- Genome-scale modelling of microbial metabolism with temporal and spatial resolution.Biochemical Society transactions · 2015Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico.
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Registered trials
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.