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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

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Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study.

Moritz von Stosch^1,2, Jan-Martijn Hamelink^3,4, Rui Oliveira⁵

¹CEAM, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. moritz.von-stosch@ncl.ac.uk.

Bioprocess and Biosystems Engineering

|February 17, 2016

Summary

This study introduces hybrid semi-parametric modeling for biopharmaceutical manufacturing, integrating dynamic models with artificial neural networks. This approach enhances process understanding for consistent high-quality production of biopharmaceuticals.

Keywords:

Dynamic modeling E. coli High cell density fermentation Hybrid modeling Upstream bioprocess development/optimization

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Area of Science:

Biotechnology
Chemical Engineering
Computational Biology

Background:

Process understanding is crucial for biopharmaceutical manufacturing, emphasized by Process Analytical Technology (PAT) and Quality by Design (QbD).
Traditional methods rely on Design of Experiments (DoE) and statistical analysis, which can be limited in capturing complex biological dynamics.
Hybrid modeling offers a flexible alternative, balancing model complexity with available data and prior knowledge.

Purpose of the Study:

To investigate hybrid semi-parametric modeling as an alternative to pure statistical data analysis for biopharmaceutical process understanding.
To develop a model integrating parametric and nonparametric components for describing bioreactor performance.
To enhance the manufacturing of high-quality biopharmaceuticals through improved process comprehension.

Main Methods:

Developed a hybrid model combining a parametric dynamic bioreactor model with a nonparametric artificial neural network.
The artificial neural network component models biomass and product formation rates.
Utilized fed-batch fermentation data from E. coli for high cell density heterologous protein production under varied conditions (induction temperature, pH, feed rates).

Main Results:

The hybrid model accurately described biomass growth and product formation across tested fermentation conditions.
Model analysis revealed product expression is influenced by early induction phase conditions.
Increased productivity negatively impacted product expression, potentially due to cytoplasmic product accumulation.

Conclusions:

Hybrid semi-parametric modeling provides a robust framework for achieving process understanding in biopharmaceutical manufacturing.
The dynamic nature of the model enables informed process timing decisions and assessment of parameter variations.
This approach supports the consistent production of high-quality biopharmaceuticals by enabling dynamic process control and optimization.