Three open Master’s Theses

Supervisor: Jürgen Zanghellini | Department of Analytical Chemistry, University of Vienna

Background

Viruses rely on host-cell metabolism to synthesize the nucleic acids, proteins, lipids, and energy required for progeny production. Human genome-scale metabolic models (GEMs) can represent these demands by incorporating virus-specific biomass or reproduction reactions. However, conventional GEMs do not capture how viral proteins actively reprogrammetabolism through interactions with host enzymes, transcription factors, and signalling pathways. Building on recent regulatory–metabolic modelling approaches, this project will augment a human GEM with both viral reproduction reactions and regulatory effects mediated by viral proteins. The model will distinguish metabolic changes caused by viral biomass production from those caused by active viral regulation, helping identify host metabolic dependencies and potential antiviral targets.

Aims of the project

This project includes the following aims:

1. Reconstruct constextualised human GEMs of specific tissues based on transcriptomics data.
2. Add reactions and regulatory reactions of selected viruses to contextualised GEMs.
3. Analyse and compare different states in the reproduction cycle of viruses.

The duration of this project is set to 9 months.

What we offer

• Great working atmosphere in a young and dynamic group working on computational biology
• Access to the state-of-the-art computational environment
• Co-supervision by a postdoctoral researcher
• Participation in group meetings and discussions about projects within the group or beyond

For applications, please contact juergen.zanghellini@univie.ac.at.

Supervisor: Jürgen Zanghellini | Department of Analytical Chemistry, University of Vienna

Background

Food authentication has become increasingly important due to widespread mislabeling and adulteration, particularly in high-value products such as honey and olive oil. These products are often diluted with arti- ficial substances or falsely labeled in terms of geographical origin, undermining consumer trust and regu- latory standards.

Omics technologies provide a powerful approach to address this challenge by enabling detailed molecular characterization of food samples. Genomics can reveal phylogenetic signatures linked to production environments, while metabolomics captures chemical profiles reflective of composition and origin.

This project aims to leverage these data types in combination with machine learning methods to de- velop robust computational tools for food authentication. By training models on publicly available datasets, the approach will enable automated classification of authenticity and geographic origin for honey and olive oil samples.

Ultimately, the project seeks to provide scalable, data-driven solutions to detect food fraud and support quality control in the food industry.

Aims of the project

This project includes the following aims:

1. Collect publicly available multi omics data of honey and olive oil samples
2. Set up libraries for authentic honey and olive oil based on the geographic origin using genomics and metabolomics
3. Develop ML tools for classification based on origin and authenticity

The duration of this project is set to 9 months.

What we offer

• Great working atmosphere in a young and dynamic group working on computational biology
• Access to the state-of-the-art computational environment
• Co-supervision by a postdoctoral researcher
• Participation in group meetings and discussions about projects within the group or beyond

For applications, please contact juergen.zanghellini@univie.ac.at.

Supervisor: Jürgen Zanghellini | Department of Analytical Chemistry, University of Vienna

Background

Genome-scale metabolic models (GEMs) are computational representations of an organism’s metabolic network, integrating known biochemical reactions and gene–protein–reaction associations to enable systemlevel analysis of metabolism.

Within a GEM, flux sampling is the gold-standard approach, used to explore the range of feasible flux distributions under given constraints. Despite its potential, flux sampling faces several challenges: existing methods can produce thermodynamically infeasible loops in flux distributions, suffer from poor convergence, and often depend strongly on the structure and size of the solution space.

In addition, flux sampling for community GEMs remains less established and requires systematic benchmarking. The aim of this project is to implement and evaluate currently available flux sampling tools and to investigate alternative sampling approaches using well-established GEMs.

Aims of the project

This project includes the following aims:

1. Collecting GEMs published in unusable formats, like excel, from literature
2. Set up an agentic AI based workflow to reconstruct usable GEMs
3. Validate curated GEMs using state of the art tools in metabolic modeling
4. Provide an open source database with all curated GEMs

The duration of this project is set to 9 months.

What we offer

• Great working atmosphere in a young and dynamic group working on computational biology
• Access to the state-of-the-art computational environment
• Co-supervision by a PhD student and a postdoctoral researcher
• Participation in group meetings and discussions about projects within the group or beyond

For applications, please contact juergen.zanghellini@univie.ac.at.

Join us!

Join us!

We extend an invitation to Postdoctoral, Doctoral, Master's, and Bachelor's students with interest in bio/cheminformatics, network analysis, and computational biology.

• Bio/cheminformatics
  • computational mass spectrometry
  • high-performance computing
• Network analysis (constraint-based)
  • modeling of metabolism
  • multi-omics integration
  • rational cell factory design
  • optimal fermentation design
• Computational biology
  • theory of microbial (community) growth

Broaden your expertise, create new knowledge, and excel as a member of our research group. To take the next step, please send your application to juergen.zanghellini@univie.ac.at.