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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Comparison of Modeling Methods for DoE-Based Holistic Upstream Process Characterization.

Benjamin Bayer1, Moritz von Stosch2, Gerald Striedner1

  • 1Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190, Austria.

Biotechnology Journal
|February 6, 2020
PubMed
Summary
This summary is machine-generated.

Hybrid modeling combined with design of experiments (DoE) offers superior bioprocess characterization over traditional methods. This dynamic approach improves understanding of process variations and enables better control for biopharmaceutical production.

Keywords:
Quality by Designhybrid modelingprocess control

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Area of Science:

  • Biotechnology
  • Bioprocess Engineering
  • Computational Biology

Background:

  • Upstream bioprocess characterization is often time-consuming and resource-intensive.
  • Traditional methods like design of experiments (DoE) with response surface models (RSMs) often overlook crucial process dynamics and temporal variations.
  • A deeper understanding of biological subsystem variations and production dynamics is needed for robust process characterization.

Purpose of the Study:

  • To propose and evaluate a hybrid modeling approach for bioprocess characterization, integrating DoE with dynamic models.
  • To compare the performance of hybrid models against pure data-driven models and traditional RSMs for process endpoints.
  • To assess the capability of time-resolved hybrid models for simultaneously predicting biomass and titer, and capturing dynamic trajectories.

Main Methods:

  • Application of DoE studies combined with hybrid modeling for process characterization.
  • Utilizing Escherichia coli fed-batch cultivations at a 20L scale to evaluate three critical process parameters.
  • Comparative analysis of hybrid models against pure black-box models and RSMs, focusing on prediction accuracy and time-resolved trajectories.

Main Results:

  • Hybrid models demonstrated superior performance in bioprocess characterization compared to pure data-driven models and RSMs.
  • The hybrid modeling approach accurately predicted biomass and titer endpoints and captured time-resolved process trajectories.
  • The study confirmed the enhanced understanding of process variations derived from dynamic modeling.

Conclusions:

  • Hybrid modeling integrated with DoE provides a more comprehensive approach to bioprocess characterization than traditional methods.
  • Time-resolved hybrid models offer significant potential for accurate soft-sensing and model predictive control in biopharmaceutical manufacturing.
  • This approach enhances process understanding by accounting for dynamic behaviors and biological variations.