13CFLUX -- Third-generation high-performance engine for isotopically (non)stationary 13C metabolic flux analysis
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908564996489216 |
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| author | Stratmann, Anton Beyß, Martin Jadebeck, Johann F. Wiechert, Wolfgang Nöh, Katharina |
| author_facet | Stratmann, Anton Beyß, Martin Jadebeck, Johann F. Wiechert, Wolfgang Nöh, Katharina |
| contents | 13C-based metabolic flux analysis (13C-MFA) is a cornerstone of quantitative systems biology, yet its increasing data complexity and methodological diversity place high demands on simulation software. We introduce 13CFLUX(v3), a third-generation simulation platform that combines a high-performance C++ engine with a convenient Python interface. The software delivers substantial performance gains across isotopically stationary and nonstationary analysis workflows, while remaining flexible to accommodate diverse labeling strategies and analytical platforms. Its open-source availability facilitates seamless integration into computational ecosystems and community-driven extension. By supporting multi-experiment integration, multi-tracer studies, and advanced statistical inference such as Bayesian analysis, 13CFLUX provides a robust and extensible framework for modern fluxomics research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23847 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 13CFLUX -- Third-generation high-performance engine for isotopically (non)stationary 13C metabolic flux analysis Stratmann, Anton Beyß, Martin Jadebeck, Johann F. Wiechert, Wolfgang Nöh, Katharina Quantitative Methods 13C-based metabolic flux analysis (13C-MFA) is a cornerstone of quantitative systems biology, yet its increasing data complexity and methodological diversity place high demands on simulation software. We introduce 13CFLUX(v3), a third-generation simulation platform that combines a high-performance C++ engine with a convenient Python interface. The software delivers substantial performance gains across isotopically stationary and nonstationary analysis workflows, while remaining flexible to accommodate diverse labeling strategies and analytical platforms. Its open-source availability facilitates seamless integration into computational ecosystems and community-driven extension. By supporting multi-experiment integration, multi-tracer studies, and advanced statistical inference such as Bayesian analysis, 13CFLUX provides a robust and extensible framework for modern fluxomics research. |
| title | 13CFLUX -- Third-generation high-performance engine for isotopically (non)stationary 13C metabolic flux analysis |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2509.23847 |