Background: In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.
Results: The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production.
Conclusion: The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2728707 | PMC |
http://dx.doi.org/10.1186/1475-2859-8-43 | DOI Listing |
Nat Commun
January 2025
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
ATR plays key roles in cellular responses to DNA damage and replication stress, a pervasive feature of cancer cells. ATR inhibitors (ATRi) are in clinical development for treating various cancers, including those with high replication stress, such as is elicited by ARID1A deficiency, but the cellular mechanisms that determine ATRi efficacy in such backgrounds are unclear. Here, we have conducted unbiased genome-scale CRISPR screens in ARID1A-deficient and proficient cells treated with ATRi.
View Article and Find Full Text PDFCell Syst
December 2024
Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Electronic address:
While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning.
View Article and Find Full Text PDFSynth Syst Biotechnol
December 2024
Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
, a widely utilized model organism, has seen continuous updates to its genome-scale metabolic model (GEM) to enhance the prediction performance for metabolic engineering and systems biology. This study presents an auxotrophy-based curation of the yeast GEM, enabling facile upgrades to yeast GEMs in future endeavors. We illustrated that the curation bolstered the predictive capability of the yeast GEM particularly in predicting auxotrophs without compromising accuracy in other simulations, and thus could be an effective manner for GEM refinement.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, California, United States of America.
The denitrifying bacterium Thauera sp. MZ1T, a common member of microbial communities in wastewater treatment facilities, can produce different compounds from a range of carbon (C) and nitrogen (N) sources under aerobic and anaerobic conditions. In these different conditions, Thauera modifies its metabolism to produce different compounds that influence the microbial community.
View Article and Find Full Text PDFBioinform Adv
November 2024
Aix-Marseille University, CNRS, IBDM UMR7288, Turing Center for Living Systems (CENTURI), Marseille 13009, France.
Motivation: Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!