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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

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Multi-objective optimal control of dynamic bioprocesses using ACADO Toolkit.

Filip Logist¹, Dries Telen, Boris Houska

¹Department of Chemical Engineering, BioTeC and Optimization in Engineering Center, KU Leuven W. de Croylaan, Belgium. filip.logist@cit.kuleuven.be

Bioprocess and Biosystems Engineering

|August 1, 2012

Summary

Optimizing dynamic bioprocesses involves multiple objectives, resulting in a set of Pareto optimal solutions. The ACADO toolkit efficiently generates these sets for decision-makers to evaluate alternatives.

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Area of Science:

Bioprocess Engineering
Optimization
Computational Biology

Background:

Dynamic bioprocesses often present complex optimization challenges with conflicting objectives.
This leads to a set of Pareto optimal solutions rather than a single optimum.
Decision-makers require efficient methods to navigate and select from these optimal alternatives.

Purpose of the Study:

To demonstrate the application of the ACADO Multi-Objective Toolkit for dynamic bioprocess optimization.
To assess the toolkit's efficiency and accuracy in generating Pareto sets for bioprocess examples.
To provide decision-makers with systematically generated optimal alternatives.

Main Methods:

Utilized the ACADO Multi-Objective Toolkit for multi-objective optimization of dynamic bioprocesses.
Integrated scalarization strategies like Normal Boundary Intersection and Normalized Normal Constraint.
Employed deterministic dynamic optimization approaches such as single and multiple shooting.

Main Results:

Successfully generated Pareto sets for multiple dynamic bioprocess examples using the ACADO toolkit.
Confirmed the toolkit's capability for efficient and accurate Pareto set generation.
The generated Pareto sets are provided as supplementary material.

Conclusions:

The ACADO Multi-Objective Toolkit is an effective tool for optimizing dynamic bioprocesses with multiple objectives.
Efficient generation of Pareto sets aids decision-makers in evaluating optimal solutions.
This approach facilitates systematic selection among trade-off alternatives in bioprocess design and operation.