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Summary: COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.
Availability And Implementation: https://doi.org/10.17881/ZKCR-BT30.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8796381 | PMC |
http://dx.doi.org/10.1093/bioinformatics/btab782 | DOI Listing |
New Phytol
September 2025
Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany.
Comparative molecular and physiological analyses of organisms from one taxonomic group grown under similar conditions offer a strategy to identify gene targets for trait improvement. While this strategy can also be performed in silico using genome-scale metabolic models for the compared organisms, we continue to lack solutions for the de novo generation of such models, particularly for eukaryotes. To facilitate model-driven identification of gene targets for growth improvement in green algae, here we present a semiautomated platform for de novo generation of genome-scale algal metabolic models.
View Article and Find Full Text PDFFront Syst Biol
July 2024
Biochemical and Biophysical Systems Group, Lawrence Livermore National Laboratory, Livermore, CA, United States.
Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes.
View Article and Find Full Text PDFMolecules
July 2025
Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan.
Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
August 2025
Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam DE14476, Germany.
Understanding the molecular mechanisms behind plant response to stress can enhance breeding strategies and help us design crop varieties with improved stress tolerance, yield, and quality. To investigate resource redistribution from growth- to defense-related processes in an essential tuber crop, potato, here we generate a large-scale compartmentalized genome-scale metabolic model (GEM), potato-GEM. Apart from a large-scale reconstruction of primary metabolism, the model includes the full known potato secondary metabolism, spanning over 566 reactions that facilitate the biosynthesis of 182 distinct potato secondary metabolites.
View Article and Find Full Text PDFBioinform Adv
May 2025
Centre of Biological Engineering, University of Minho, Braga 4710-057, Portugal.
Summary: The increasing availability of high-throughput technologies in systems biology has advanced predictive tools like genome-scale metabolic models. Despite this progress, integrating omics data to create accurate, context-specific metabolic models for different tissues or cells remains challenging. A significant issue is that many existing tools rely on proprietary software, which limits accessibility.
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