ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions
Summary: Reliable genome-scale metabolic models (GEMs) of metabolic processes are important for understanding cellular behavior. However, the presence of thermodynamically infeasible cycles (TICs) limits their predictive ability. We present ThermOptCOBRA, a comprehensive solution consisting of four...
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Elsevier
2025-08-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225012660 |
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| author | Pavan Kumar S Nirav Pravinbhai Bhatt |
| author_facet | Pavan Kumar S Nirav Pravinbhai Bhatt |
| author_sort | Pavan Kumar S |
| collection | DOAJ |
| description | Summary: Reliable genome-scale metabolic models (GEMs) of metabolic processes are important for understanding cellular behavior. However, the presence of thermodynamically infeasible cycles (TICs) limits their predictive ability. We present ThermOptCOBRA, a comprehensive solution consisting of four algorithms for optimal model construction and analysis that integrate thermodynamic constraints to address TICs. By leveraging network topology, ThermOptCOBRA efficiently identifies TICs in 7,401 published models. It determines thermodynamically feasible flux directions, thereby detecting the blocked reactions, which yields more refined models with fewer TICs. Furthermore, it constructs thermodynamically consistent context-specific models that are compact in comparison to Fastcore in 80% of cases. ThermOptCOBRA also facilitates efficient loop detection and removal from flux distributions, improving predictive accuracy across flux analysis methods. Moreover, it enhances sampling algorithms by enabling loopless sample generation. In summary, ThermOptCOBRA significantly improves TIC handling in GEMs, advancing metabolic model quality for deeper insights into cellular metabolism. |
| format | Article |
| id | doaj-art-84d8917165574880b26830b78aaa9f6f |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-84d8917165574880b26830b78aaa9f6f2025-08-20T02:47:25ZengElsevieriScience2589-00422025-08-0128811300510.1016/j.isci.2025.113005ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictionsPavan Kumar S0Nirav Pravinbhai Bhatt1BioSystems Engineering and Control (BiSECt) Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; The Centre for Integrative Biology and Systems Medicine (IBSE), Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Data Science and AI, Wadhwani School of Data Science and AI, Indian Institute of Technology Madras, Chennai, Tamil Nadu, IndiaBioSystems Engineering and Control (BiSECt) Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; The Centre for Integrative Biology and Systems Medicine (IBSE), Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Data Science and AI, Wadhwani School of Data Science and AI, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; IIT Madras Zanzibar Campus, Zanzibar, Tanzania; Corresponding authorSummary: Reliable genome-scale metabolic models (GEMs) of metabolic processes are important for understanding cellular behavior. However, the presence of thermodynamically infeasible cycles (TICs) limits their predictive ability. We present ThermOptCOBRA, a comprehensive solution consisting of four algorithms for optimal model construction and analysis that integrate thermodynamic constraints to address TICs. By leveraging network topology, ThermOptCOBRA efficiently identifies TICs in 7,401 published models. It determines thermodynamically feasible flux directions, thereby detecting the blocked reactions, which yields more refined models with fewer TICs. Furthermore, it constructs thermodynamically consistent context-specific models that are compact in comparison to Fastcore in 80% of cases. ThermOptCOBRA also facilitates efficient loop detection and removal from flux distributions, improving predictive accuracy across flux analysis methods. Moreover, it enhances sampling algorithms by enabling loopless sample generation. In summary, ThermOptCOBRA significantly improves TIC handling in GEMs, advancing metabolic model quality for deeper insights into cellular metabolism.http://www.sciencedirect.com/science/article/pii/S2589004225012660Biological sciencesApplied computing |
| spellingShingle | Pavan Kumar S Nirav Pravinbhai Bhatt ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions iScience Biological sciences Applied computing |
| title | ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions |
| title_full | ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions |
| title_fullStr | ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions |
| title_full_unstemmed | ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions |
| title_short | ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions |
| title_sort | thermoptcobra thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions |
| topic | Biological sciences Applied computing |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225012660 |
| work_keys_str_mv | AT pavankumars thermoptcobrathermodynamicallyoptimalconstructionandanalysisofmetabolicnetworksforreliablephenotypepredictions AT niravpravinbhaibhatt thermoptcobrathermodynamicallyoptimalconstructionandanalysisofmetabolicnetworksforreliablephenotypepredictions |