Cluster of Excellence Circular Bioengineering

PhD position: Computational design of optimal of bioprocesses

Fed-batch processes are a cornerstone of industrial biotechnology due to their simplicity, scalability, and precise control over critical variables. By gradually adding substrate during the feed phase without removing fermentation broth, these systems maintain optimal substrate concentrations, preventing inhibition. They can be tailored to the metabolic needs of various microorganisms and cell lines, including bacteria, yeast, and mammalian cells. This adaptability supports the biosynthesis of target products while ensuring high product quality, making fed-batch processes indispensable for producing pharmaceuticals, enzymes, biofuels, and fine chemicals.

Designing effective feeding strategies is critical for maximizing product titer, rate, yield, and quality. However, optimizing feeding profiles is challenging due to the many influencing factors. While standard approaches like exponential or constant feeding are widely used for their simplicity and practicality, they often fail to fully exploit the production potential of host systems.

Mathematical modeling provides a cost-effective way to design and refine feeding strategies by simulating profiles, predicting outcomes, and reducing the need for extensive experimental trials. However, existing tools often require advanced coding skills, making them inaccessible to many laboratory scientists.

To address this gap, this project will develop process-specific models and optimization approaches, packaged into a simple, user-friendly software suite with a graphical interface. This tool enables researchers to quickly identify and optimize fed-batch feeding strategies without the need for advanced technical expertise.

 

Research Objectives

  • Develop strain- or cell-line-specific mathematical models for production hosts
  • Create fast and scalable optimization approaches to define optimal environments
  • Investigate the integration of life-cycle aspects into the optimal design
  • Develop a user-friendly interface for the software suite
  • Continuously expand the platform to include new model systems and environments, such as continuous fermentation processes

 

Methods

  • Constraint-based modeling of metabolism
  • Data science analysis of bioprocess data (in collaboration with partners)
  • Bioprocess and mathematical modeling

 

Supervisors

Main supervisor: Univ.-Prof Jürgen Zanghellini

Co-supervisors: Univ.-Prof Diethard Mattanovich, Assistant Prof. Matthias Steiger, Univ.-Prof. Oliver Spadiut

 

Submission Deadline

31.03.2025

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