Alexandre Oliveira, Jorge Ferreira, Vítor Vieira, Bruno Sá, Miguel Rocha
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引用次数: 0
Abstract
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. We introduce TROPPO, an open-source Python library designed to overcome these challenges. TROPPO supports a wide range of context-specific reconstruction algorithms, provides validation methods for assessing generated models, and includes gap-filling algorithms to ensure model consistency, integrating well with other constraint-based tools.
Availability and implementation: TROPPO is implemented in Python and is freely available at https://github.com/BioSystemsUM/TROPPO and https://pypi.org/project/TROPPO/.
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