Cluster of Excellence Circular Bioengineering

PhD position: Optimal compartmentalization of metabolic pathways

The Cluster of Excellence Circular Bioengineering is revolutionizing how we think about sustainable production. By combining bioengineering, materials science, and chemistry, the cluster aims to establish innovative, closed-loop material cycles for zero-waste production and maximal resource efficiency.

At the heart of the cluster, three interconnected research pillars drive innovation:

  • Sustainable Natural Resource
  • Utilization Microbial and Enzymatic Conversion
  • Process Circularity

A PhD position is available in the group of Prof. Zanghellini at the Institute of Analytical Chemistry starting May 2025. The group employs mathematical modeling, artificial intelligence, and high-performance computing to study metabolic networks and optimize microbes for biotechnological applications.

The candidate's project aims to predict the optimal compartmentalization of a production pathway to improve metabolic performance. By allocating specific reactions to distinct cellular compartments, we seek to optimize thermodynamic properties, enhance reaction efficiency, and minimize energy losses. The approach leverages compartmentalization to isolate engineered pathways, concentrate substrates and enzymes, and protect the cell from toxic intermediates.

 

Your tasks

  • Develop computational tools for designing optimal synthetic communities and modularized production units within microbes.
  • Create software solutions and data processing pipelines for computational modeling, including data analysis and preparation of results for publication.

 

Your profile

  • A completed Master's degree in Data Science, Physics, Chemistry, Computational Biology, Bioinformatics, or a related field.
  • Strong programming skills in Python, R, or similar languages.
  • Experience with applying AI (ideally) to metabolic modeling is desired
  • Research expertise and proactive approach
  • A collaborative mindset and effective teamwork skills
  • Strong organizational skills with attention to precision and diligence

 

Background

Compartmentalization is a fundamental feature of cellular organization that enables precise spatial and chemical control over metabolic processes. By sequestering specific reactions into dedicated compartments, cells can optimize enzyme activity, enhance reaction rates, and reduce interference from competing pathways. This isolation of intermediates not only minimizes side reactions but also mitigates potential toxicity, allowing cells to perform complex and highly efficient bioconversions.

Additionally, compartments provide unique microenvironments tailored to specific biochemical needs. These include differences in pH, redox potential, and cofactor availability, which are often critical for driving reactions that would be inefficient or incompatible within the general cytosolic environment. For example, organelles such as peroxisomes, and lysosomes serve specialized metabolic roles, enabling diverse processes like fatty acid degradation, and macromolecule recycling.

In biotechnology, leveraging compartmentalization can significantly enhance the efficiency of engineered pathways. Membrane-bound compartments can isolate engineered pathways, concentrating substrates and enzymes while protecting the rest of the cell from toxic intermediates. By fine-tuning the chemical conditions within these compartments, non-native reactions that mimic natural processes can be enabled.

The goal of this project is to predict the optimal compartmentalization of a production pathway. By strategically allocating specific reactions to distinct cellular compartments, we aim to optimize the thermodynamic properties of the pathway, enhance efficiency, minimize energy losses, and improve metabolic performance.

 

Research Objectives

  • Establish a computational framework to calculate and analyze the thermodynamic profiles of biochemical pathways across various compartments.
  • Identify compartmentalization strategies that maximize thermodynamic driving forces, enhance reaction rates, and minimize energy losses within a pathway.
  • Assess the computational framework's accuracy, by comparing model predictions with available datasets (e.g., on mitochondria).
  • Incorporate practical biological constraints, such as transport efficiencies, and resource limitations, to refine the model's real-world applicability.

 

Methods

  • Constraint-based modeling of metabolism
  • Thermodynamic flux analysis

 

Supervisors

Main supervisor: Univ.-Prof Jürgen Zanghellini

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

 

Submission Deadline

31.03.2025

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