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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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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Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Applying mechanistic models in bioprocess development.

Rita Lencastre Fernandes1, Vijaya Krishna Bodla, Magnus Carlquist

  • 1Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800, Lyngby, Denmark.

Advances in Biochemical Engineering/Biotechnology
|January 12, 2013
PubMed
Summary
This summary is machine-generated.

Mechanistic modeling summarizes bioprocess knowledge for process analytical technology. Sensitivity analysis identifies critical parameters, guiding experimental design and improving process control for better outcomes.

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Computational Biology

Background:

  • Process analytical technology (PAT) relies on understanding bioprocess mechanisms.
  • Mechanistic modeling offers a robust method to summarize existing process knowledge.
  • Integrating process inputs (critical process variables) with outputs (product concentration, quality) is key.

Purpose of the Study:

  • To highlight the value of mechanistic modeling in bioprocess development.
  • To demonstrate the application of uncertainty and sensitivity analysis for mechanistic models.
  • To guide experimental design by identifying critical process variables.

Main Methods:

  • Utilizing mechanistic modeling to represent bioprocess input-output relationships.
  • Applying uncertainty and sensitivity analysis to decompose output variations.
  • Case study using Saccharomyces cerevisiae fermentation data with the Sonnleitner and Käppeli model.

Main Results:

  • Mechanistic models effectively summarize process knowledge and guide experimental planning.
  • Sensitivity analysis effectively identifies input parameters driving output uncertainty.
  • The Sonnleitner and Käppeli model was analyzed, demonstrating the generic applicability of the methods.

Conclusions:

  • Mechanistic modeling combined with sensitivity analysis is a powerful tool for bioprocess optimization.
  • This approach reduces experimental effort by focusing on critical parameters.
  • The methodology is transferable to diverse and complex bioprocess systems.