ReviewNPJ systems biology and applications2020
Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective.
Review in NPJ systems biology and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Systematic data-driven genome-scale metabolic model reduction for bioprocess modeling: CHO culture case study.NPJ systems biology and applications · 2026Article
- A Hybrid Modeling Framework for Predictive Digital Twins of CHO Cell Culture.Computational and structural biotechnology journal · 2026Article
- Defined metabolic states shape T cell fate and function across culture conditions.Frontiers in immunology · 2025Article
- Driving towards digital biomanufacturing by CHO genome-scale models.Trends in biotechnology · 2024Review
- Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways.PLoS computational biology · 2024Article
- CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities.Bioinformatics (Oxford, England) · 2023Article
- Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Review
- The Use of Interdisciplinary Approaches to Understand the Biology ofMicroorganisms · 2022Review
- Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action.Microbial biotechnology · 2022Article
- Innovative vaccine platforms against infectious diseases: Under the scope of the COVID-19 pandemic.International journal of pharmaceutics · 2021Review
- The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale.Microorganisms · 2020Review
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.
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