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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Generic Workflow for the Setup of Mechanistic Process Models.

Sven Daume1, Sandro Kofler1, Julian Kager1

  • 1Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Vienna, Austria.

Methods in Molecular Biology (Clifton, N.J.)
|December 21, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a workflow for developing mechanistic process models, crucial for process optimization and control. The method iteratively refines reaction kinetics for reliable cell culture process modeling.

Keywords:
Cell culture modelCovariance and correlationIdentifiability analysisKinetic modelMechanistic modelParameter errorSensitivity

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Area of Science:

  • Biotechnology
  • Chemical Engineering
  • Process Systems Engineering

Background:

  • Mechanistic process models offer transferable knowledge for process development, optimization, and control.
  • Reliable model development is critical for successful simulation and application in complex systems.
  • Cell culture processes require accurate models for predicting and managing critical variables.

Purpose of the Study:

  • To present a workflow for generating mechanistic process models.
  • To apply this workflow to a representative cell culture process.
  • To demonstrate the iterative approach for defining reactions and kinetics.

Main Methods:

  • Defining critical reactions within the process.
  • Identifying and testing various reaction kinetics.
  • Iterative model refinement and quality assessment.
  • Application to a standard cell culture process.

Main Results:

  • A structured workflow for mechanistic process model generation was successfully applied.
  • The iterative testing of reactions and kinetics led to a target-oriented model.
  • The developed model provides a basis for simulation and control of cell culture processes.

Conclusions:

  • The presented workflow enables the development of reliable mechanistic process models.
  • Accurate kinetic identification is key to successful process modeling.
  • This approach facilitates process optimization and control in cell culture systems.