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A bootstrap-aggregated hybrid semi-parametric modeling framework for bioprocess development.

José Pinto1, Cristiana Rodrigues de Azevedo1,2, Rui Oliveira1

  • 1REQUIMTE/DQ, Faculty of Science and Technology, University Nova de Lisboa, Campus de Caparica, 2829-516, Caparica, Portugal.

Bioprocess and Biosystems Engineering
|August 4, 2019
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Summary
This summary is machine-generated.

Bootstrap aggregation enhances hybrid semi-parametric models for bioprocess optimization. This machine learning technique improves predictive accuracy in Escherichia coli fed-batch cultures, leading to better cell growth and protein expression insights.

Keywords:
BaggingData portioningDesign of experimentsEnsemble methodsHybrid modelingHybrid semi-parametric modelingSampling error

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Area of Science:

  • Biotechnology and Biochemical Engineering
  • Computational Biology
  • Process Systems Engineering

Background:

  • Hybrid semi-parametric models integrate mechanistic and machine learning approaches for robust process development.
  • Statistical Design of Experiments (DoE) is crucial for efficient data generation in complex biological systems.
  • Optimizing fed-batch fermentation of Escherichia coli for biomass and recombinant protein production presents significant challenges.

Purpose of the Study:

  • To introduce and evaluate bootstrap aggregation as a method to enhance the predictive performance of hybrid semi-parametric models.
  • To compare the efficacy of bootstrap aggregation across different statistical designs (Box-Behnken, central composite, Doehlert) for process optimization.
  • To assess the impact of these models on identifying optimal conditions for Escherichia coli growth and protein expression.

Main Methods:

  • Development of hybrid semi-parametric models incorporating both mechanistic and machine learning components.
  • Application of bootstrap aggregation to ensemble multiple models for improved predictive power.
  • Utilizing synthetic datasets generated from Box-Behnken, central composite, and Doehlert designs for model training and validation.
  • Comparative analysis of aggregated versus non-aggregated models using prediction mean squared error.

Main Results:

  • Bootstrap aggregation significantly reduced the prediction mean squared error for new batch experiments across all tested designs.
  • The Doehlert design demonstrated a slight advantage in identifying the process optimum compared to Box-Behnken and central composite designs.
  • The number of aggregated models emerged as a critical parameter requiring careful calibration for optimal performance.
  • Error bounds could be computed from ensemble predictions, offering insights into model component variability.

Conclusions:

  • Bootstrap aggregation is an effective strategy for improving the predictive accuracy of hybrid semi-parametric models in bioprocess development.
  • The choice of statistical design impacts the identification of process optima, with Doehlert design showing promising results.
  • Model calibration, particularly the number of models to aggregate, is essential for maximizing benefits.
  • The developed approach provides valuable error estimation, enhancing the reliability of process optimization predictions.