ArticleEngineering in life sciences2017
Intensified design of experiments for upstream bioreactors.
Article in Engineering in life sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Hybrid modeling for industrial fermentation processes with an "Intra-Batch Experimental Design".Journal of industrial microbiology & biotechnology · 2026Article
- Direct Consideration of Process History During Intensified Design of Experiments Planning Eases Interpretation of Mammalian Cell Culture Dynamics.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing recombinant growth factor and serum protein production for cultivated meat manufacturing.Microbial cell factories · 2025Review
- Applying intensified design of experiments to mammalian cell culture processes.Engineering in life sciences · 2022Article
- Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses.Bioprocess and biosystems engineering · 2021Article
- Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments.Frontiers in bioengineering and biotechnology · 2021Article
- Automated Conditional Screening of MultipleBioengineering (Basel, Switzerland) · 2020Article
- Working within the Design Space: Do Our Static Process Characterization Methods Suffice?Pharmaceutics · 2020Article
- History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance.Computational and structural biotechnology journal · 2020Review
- Soft sensor based on 2D-fluorescence and process data enabling real-time estimation of biomass inEngineering in life sciences · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical Design of Experiments (DoE) is a widely adopted methodology in upstream bioprocess development (and generally across industries) to obtain experimental data from which the impact of independent variables (factors) on the process response can be inferred. In this work, a method is proposed that reduces the total number of experiments suggested by a traditional DoE. The method allows the evaluation of several DoE combinations to be compressed into a reduced number of experiments, which is referred to as intensified Design of Experiments (iDoE). In this paper, the iDoE is used to develop a dynamic hybrid model (consisting of differential equations and a feedforward artificial neural network) for data generated from a simulated
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.