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Results for “constraint-based modelling”

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PubMed2026

WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.

SUMMARY: Accurate enzyme turnover numbers are essential for building enzyme-constrained genome-scale metabolic models. However, collecting and curating these parameters remains a major bottleneck. Indeed, kcat values are scattered across multiple databases, reported under varying experimental conditions, and often missing for many enzymes. To address this challenge, we present WILDkCAT, a Python-based pipeline that enables the retrieval of kcat values from wild-type enzyme measured under user-specified pH and temperature ranges for a given metabolic model. The application to Escherichia coli (iML1515) and Homo sapiens (Human-GEM) models demonstrated the ability of WILDkCAT to retrieve substantial kcat coverage and its applicability across diverse genome-scale models. AVAILABILITY AND IMPLEMENTATION: WILDkCAT is available at https://github.com/sysbiolux/WILDkCAT and from PyPI. WILDkCAT works on all major operating systems and computer architectures. The documentation is available at https://sysbiolux.github.io/WILDkCAT.

Software↗

PubMed2026

Partial enumeration of extreme rays in metabolic networks using bit pattern trees.

Extreme ray analysis of metabolic networks, even though very powerful, is currently limited to smaller metabolic networks. Some approaches to generating partial sets of extreme rays exist, but the computational efficiency of the so-called double-description method is yet to be exploited. Previous work highlighted the possibility of sampling within its iterations, enabling partial enumeration for double-description based methods. However, these approaches severely lack computational efficiency to be a suitable alternative. In this work, the highly efficient bit pattern trees are used within the sampling framework to significantly enhance its output and speed. Combined with the recent revision of the Canonical Basis Approach (CBA), our approach outperforms the other tested methods under the reported benchmark conditions even for a full enumeration study, requiring only half the computation time. In addition, a filter setting allows the memory demand to be scaled down while retaining high efficiency. However, some issues with the combinatorial explosion of candidates still persist and are further investigated. This study therefore puts forward a novel, double description-based alternative to partial enumeration of extreme rays. Further improvements in memory efficiency would allow this promising approach to scale powerful extreme ray-based analyses to genome-scale metabolic networks.

constraint-based modelling↗

PubMed2025

Flux-sum coupling analysis of metabolic network models.

Metabolites acting as substrates and regulators of all biochemical reactions play an important role in maintaining the functionality of cellular metabolism. Despite advances in the constraint-based framework for genome-scale metabolic modeling, we lack reliable proxies for metabolite concentrations that can be efficiently determined and that allow us to investigate the relationship between metabolite concentrations in specific metabolic states in the absence of measurements. Here, we introduce a constraint-based approach, the flux-sum coupling analysis (FSCA), which facilitates the study of the interdependencies between metabolite concentrations by determining coupling relationships based on the flux-sum of metabolites. Application of FSCA on metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana showed that the three coupling relationships are present in all models and pinpointed similarities in coupled metabolite pairs. Using the available concentration measurements of E. coli metabolites, we demonstrated that the coupling relationships identified by FSCA can capture the qualitative associations between metabolite concentrations and that flux-sum is a reliable proxy for metabolite concentration. Therefore, FSCA provides a novel tool for exploring and understanding the intricate interdependencies between the metabolite concentrations, advancing the understanding of metabolic regulation, and improving flux-centered systems biology approaches.

Escherichia coli↗

PubMed2026

Diet modulates cardiac metabolic stress during anthracycline treatment.

Diet is a modifiable determinant of cardiovascular risk and may influence tolerance to cancer therapies. The mechanisms by which specific dietary components affect cardiac metabolism during anthracycline treatment remain poorly defined, limiting the incorporation of dietary recommendations into treatment guidelines. Here, we integrated heart proteomics data from patients treated with or without anthracyclines with a genome-scale reconstruction of human cardiac metabolism (CardioNet). Using constraint-based flux analysis, we conducted >30,000 in silico simulations of diet scenarios generated from chemical profiles of ∼500 foods curated in the Periodic Table of Food Initiative. These simulations revealed that diets enriched in rapidly absorbable sugars and depleted of essential fatty acids impair cardiac metabolic efficiency, increasing reactive oxygen species production and the demand for purine salvage fluxes. These predicted metabolic patterns were consistent with plasma metabolomics from patients treated with anthracyclines, validating our findings. Computational modeling of 39 recipes across six cuisines revealed cardiometabolic effects of omnivorous versus vegan diets in patients. Modeling of a healthy vegan diet increased cardiometabolic efficiency compared with a healthy omnivorous diet in patients treated with anthracyclines, independent of the culinary background. Our approach demonstrates that integrating the molecular composition of food with genome-scale metabolic models enables systematic analysis of diet patterns for translational testing. Ultimately, these in silico studies provide a framework for trials and may inform dietary recommendations for improving cardiometabolic health.NEW & NOTEWORTHY We developed a systems biology framework to predict how diet influences cardiac metabolism during cancer therapy. Across >30,000 in silico diet simulations, we identified nutrient patterns that either exacerbate or mitigate anthracycline-induced metabolic stress. These findings demonstrate how computational modeling can uncover diet-metabolism interactions driving cardiotoxicity and guide dietary interventions.

Humans↗