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Fed-Batch Culture

Fed-batch culture is a widely used bioprocessing strategy combining aspects of batch culture with controlled substrate feeding to optimize cell growth and product formation. In this semi-closed system, nutrients are strategically added during fermentation, while the accumulated products and biomass remain within the bioreactor until the end of the operation. This controlled addition of substrates allows for better management of growth kinetics, nutrient limitation, and metabolite...
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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 squares (OLS)...
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Model-based analysis on the extractability of information from data in dynamic fed-batch experiments.

Patrick Wechselberger1, Patrick Sagmeister, Christoph Herwig

  • 1Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Vienna, Austria.

Biotechnology Progress
|November 6, 2012
PubMed
Summary

Accurately calculating bioprocess rates requires careful consideration of temporal resolution and measurement errors. This study presents a rule of thumb for estimating signal-to-noise ratio (SNR) and a data reconciliation method for dynamic fed-batch experiments.

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Area of Science:

  • Biotechnology
  • Bioprocess Engineering
  • Chemical Engineering

Background:

  • Dynamic changes in bioprocess parameters like specific growth rate (μ) are common in industrial manufacturing and development.
  • Quantitative descriptions of these variations are crucial for understanding culture physiology.
  • Accurate rate calculations are essential for effective bioprocess monitoring and control.

Purpose of the Study:

  • To identify limitations and challenges in calculating rates from dynamic fed-batch experiments, particularly concerning temporal resolution.
  • To evaluate the impact of measurement errors, temporal resolution, and physiological activity on the signal-to-noise ratio (SNR) of calculated rates.
  • To introduce practical tools for improving the reliability of rate calculations in bioprocesses.

Main Methods:

  • An in-silico approach was used to evaluate the impact of various factors on the SNR of calculated rates.
  • A generally applicable rule of thumb equation was developed for estimating the SNR of specific rates.
  • A generic data reconciliation approach was presented to remove random and systematic errors from bioprocess data.

Main Results:

  • The study demonstrated the significant influence of measurement errors and temporal resolution on the SNR of calculated rates.
  • A practical rule of thumb equation was provided for estimating SNR, aiding in the definition of sampling intervals and statistical significance assessment.
  • A reconciliation technique was validated for its effectiveness in removing errors from fed-batch culture data.

Conclusions:

  • Accurate calculation of bioprocess rates in dynamic fed-batch systems is challenging due to temporal resolution and measurement errors.
  • The proposed rule of thumb equation and data reconciliation approach offer practical solutions for improving rate calculation accuracy and data reliability.
  • These tools are valuable for bioprocess development, monitoring, and ensuring statistically significant results in E. coli fed-batch cultures.