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Modelado metabólico y enfoques basados en datos para la innovación en bioprocesos en la era de la Industria 4.0: Revisión
Metabolic Modeling and Data-Driven Approaches for Bioprocess Innovation in the Industry 4.0 era: A Review
DOI:
https://doi.org/10.15446/rev.colomb.biote.v28n1.122181Palabras clave:
Metabolic modeling, Systems biology, Data mining, Bioprocess optimization, Synthetic biology (es)Descargas
El modelado metabólico se ha convertido en una herramienta clave para comprender y optimizar las funciones celulares en bioprocesos industriales. En el marco de la Industria 4.0, la integración del modelado computacional, las tecnologías de alto rendimiento y el análisis de grandes volúmenes de datos está transformando tanto la investigación como la biotecnología industrial. Los modelos metabólicos a escala del genoma (GEMs), basados en restricciones estequiométricas, permiten integrar datos ómicos para predecir distribuciones de flujos intracelulares y relaciones genotipo–fenotipo en diversas condiciones. Sin embargo, los enfoques puramente mecanísticos a menudo no logran capturar la regulación dinámica y las complejas interacciones metabólicas. Para abordar estas limitaciones, se están adoptando estrategias híbridas que combinan el modelado dinámico con técnicas basadas en datos, incluyendo minería de datos y aprendizaje automático. La convergencia de la ingeniería metabólica, la biología de sistemas y la biología sintética con métodos computacionales avanzados está acelerando el diseño racional de fábricas celulares microbianas y el desarrollo de bioprocesos más eficientes y sostenibles. Esta revisión destaca avances recientes en modelado metabólico y enfoques integrados de análisis de datos, enfatizando su potencial para mejorar la eficiencia de los bioprocesos y fomentar la innovación en la era de la Industria 4.0.
Metabolic modeling has become a key tool for understanding and optimizing cellular functions in industrial bioprocesses. Within the framework of Industry 4.0, the integration of computational modeling, high-throughput technologies, and big data analytics is reshaping both research and industrial biotechnology. Genome-scale metabolic models (GEMs), based on stoichiometric constraints, enable the integration of omics data to predict intracellular flux distributions and genotype–phenotype relationships across diverse conditions. However, purely mechanistic approaches often fail to capture dynamic regulation and complex metabolite interactions. To address these limitations, hybrid strategies that combine dynamic modeling with data-driven techniques, including data mining and machine learning, are increasingly being adopted. The convergence of metabolic engineering, systems biology, and synthetic biology with advanced computational methods is accelerating the rational design of microbial cell factories and the development of more efficient and sustainable bioprocesses. In this context, this review highlights recent advances in metabolic modeling and integrative data analysis approaches, emphasizing their potential to enhance bioprocess efficiency and foster innovation in the Industry 4.0 era.
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