Reconstructing life

As the knowledge of genes, proteins, and other biological components has developed, so has the interest in studying the interaction between them in order to understand complete biological systems. Here, the systems biology approach provides concepts and tools. The ultimate goal is to rebuild nature on the computer and set up a complete mathematical model of living cells in silico. Such models may then, for instance, be used to optimize the biotechnological production of value-added chemicals by microorganisms.

The research focuses on mathematically modeling the dynamics, regulation, and control of metabolic networks. In particular, we are interested in studying structural, i.e. topological properties of complex metabolic networks and how these structures give rise to metabolic functions. We develop computational tools to predict optimized microbes for biotechnological applications. Most of the experimental work is carried out in collaboration with other research groups within acib.


Latest publications

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Kevin Mildau, Henry Ehlers, Ian Oesterle, Manuel Pristner, Benedikt Warth, Maria Doppler, Christoph Bueschl, Jürgen Zanghellini, and Justin J. J. van der Hooft. 2024. Analytical Chemistry, 96, 15, 5798–5806.

Abstract: Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

PyCoMo: a python package for community metabolic model creation and analysis

Michael Predl, Marianne Mießkes, Thomas Rattei, and Jürgen Zanghellini. 2024. Bioinformatics, 40, 4.

Abstract: PyCoMo is a python package for quick and easy generation of genome-scale compartmentalized community metabolic models that are compliant with current openCOBRA file formats. The resulting models can be used to predict (i) the maximum growth rate at a given abundance profile, (ii) the feasible community compositions at a given growth rate, and (iii) all exchange metabolites and cross-feeding interactions in a community metabolic model independent of the abundance profile; we demonstrate PyCoMo’s capability by analysing methane production in a previously published simplified biogas community metabolic model.

Characterising the metabolic rewiring of extremely slow growing Komagataella phaffii

Benjamin Luke Coltman, Corinna Rebnegger, Brigitte Gasser, and Jürgen Zanghellini. Microbial Biotechnology, 17, 1.

Abstract: Retentostat cultivations have enabled investigations into substrate-limited near-zero growth for a number of microbes. Quantitative physiology at these near-zero growth conditions has been widely discussed, yet characterisation of the fluxome is relatively under-reported. We investigated the rewiring of metabolism in the transition of a recombinant protein-producing strain of Komagataella phaffii to glucose-limited near-zero growth rates. We used cultivation data from a 200-fold range of growth rates and comprehensive biomass composition data to integrate growth rate dependent biomass equations, generated using a number of different approaches, into a K. phaffii genome-scale metabolic model. Here, we show that a non-growth-associated maintenance value of 0.65 (Formula presented.) and a growth-associated maintenance value of 108 (Formula presented.) lead to accurate growth rate predictions. In line with its role as energy source, metabolism is rewired to increase the yield of ATP per glucose. This includes a reduction of flux through the pentose phosphate pathway, and a greater utilisation of glycolysis and the TCA cycle. Interestingly, we observed activity of an external, non-proton translocating NADH dehydrogenase in addition to the malate–aspartate shuttle. Regardless of the method used for the generation of biomass equations, a similar, yet different, growth rate dependent rewiring was predicted. As expected, these differences between the different methods were clearer at higher growth rates, where the biomass equation provides a much greater constraint than at slower growth rates. When placed on an increasingly limited glucose diet, the metabolism of K. phaffii adapts, enabling it to continue to drive critical processes sustaining its high viability at near-zero growth rates.

Costs of ribosomal RNA stabilization affect ribosome composition at maximum growth rate

Diana Széliová, Stefan Müller, and Jürgen Zanghellini. 2024. Commun Biol 7, 196.

Abstract: Ribosomes are key to cellular self-fabrication and limit growth rate. While most enzymes are proteins, ribosomes consist of 1/3 protein and 2/3 ribonucleic acid (RNA) (in E. coli).

Here, we develop a mechanistic model of a self-fabricating cell, validated across diverse growth conditions. Through resource balance analysis (RBA), we explore the variation in maximum growth rate with ribosome composition, assuming constant kinetic parameters.

Our model highlights the importance of RNA instability. If we neglect it, RNA synthesis is always cheaper than protein synthesis, leading to an RNA-only ribosome at maximum growth rate. Upon accounting for RNA turnover, we find that a mixed ribosome composed of RNA and proteins maximizes growth rate. To account for RNA turnover, we explore two scenarios regarding the activity of RNases. In (a) degradation is proportional to RNA content. In (b) ribosomal proteins cooperatively mitigate RNA instability by protecting it from misfolding and subsequent degradation. In both cases, higher protein content elevates protein synthesis costs and simultaneously lowers RNA turnover expenses, resulting in mixed RNA-protein ribosomes. Only scenario (b) aligns qualitatively with experimental data across varied growth conditions.

Our research provides fresh insights into ribosome biogenesis and evolution, paving the way for understanding protein-rich ribosomes in archaea and mitochondria.

Sulfate limitation increases specific plasmid DNA yield in E. coli fed-batch processes

Mathias Gotsmy, Florian Strobl, Florian Weiß, Petra Gruber, Barbara Kraus, Jürgen Mairhofer, and Jürgen Zanghellini. 2023. Microb Cell Fact 22, 242.

Abstract: Plasmid DNA (pDNA) is a key biotechnological product whose importance became apparent in the last years due to its role as a raw material in the messenger ribonucleic acid (mRNA) vaccine manufacturing process. In pharmaceutical production processes, cells need to grow in the defined medium in order to guarantee the highest standards of quality and repeatability. However, often these requirements result in low product titer, productivity, and yield.

In this study, we used constraint-based metabolic modeling to optimize the average volumetric productivity of pDNA production in a fed-batch process. We identified a set of 13 nutrients in the growth medium that are essential for cell growth but not for pDNA replication. When these nutrients are depleted in the medium, cell growth is stalled and pDNA production is increased, raising the specific and volumetric yield and productivity. To exploit this effect we designed a three-stage process (1. batch, 2. fed-batch with cell growth, 3. fed-batch without cell growth). The transition between stage 2 and 3 is induced by sulfate starvation. Its onset can be easily controlled via the initial concentration of sulfate in the medium.

We validated the decoupling behavior of sulfate and assessed pDNA quality attributes (supercoiled pDNA content) in E. coli with lab-scale bioreactor cultivations. The results showed an increase in supercoiled pDNA to biomass yield by 29% upon limitation of sulfate.

In conclusion, even for routinely manufactured biotechnological products such as pDNA, simple changes in the growth medium can significantly improve the yield and quality.


  • Genome-scale metabolic models predict growth decoupling strategies
  • Sulfate limitation decouples cell growth from pDNA production
  • Sulfate limitation increases the specific supercoiled pDNA yield by 33% and the volumetric productivity by 13%.
  • We propose that sulfate limitation improves the biosynthesis of over 25% of naturally secreted products in E. coli.

Optimizing VLP production in gene therapy: Opportunities and challenges for in silico modeling

Leopold Zehetner, Diana Széliová, Barbara Kraus, Michael Graninger, Jürgen Zanghellini, and Juan A. Hernandez Bort. 2023. Biotechnology J.

Abstract: Over the past decades, virus-like particle (VLP)-based gene therapy (GT) evolved as a promising approach to cure inherited diseases or cancer. Tremendous costs due to inefficient production processes remain one of the key challenges despite considerable efforts to improve titers. This review aims to link genome-scale metabolic models (GSMMs) to cell lines used for VLP synthesis for the first time. We summarize recent advances and challenges of GSMMs for Chinese hamster ovary (CHO) cells and provide an overview of potential cell lines used in GT. Although GSMMs in CHO cells led to significant improvements in growth rates and recombinant protein (RP)-production, no GSMM has been established for VLP production so far. To facilitate the generation of GSMM for these cell lines we further provide an overview of existing omics data and the highest production titers so far reported.

ecmtool: fast and memory-efficient enumeration of elementary conversion modes

Bianca Buchner, Tom J. Clement, Daan H. de Groot, and Jürgen Zanghellini. 2023. Bioinformatics, 39, 3.

Motivation: Characterizing all steady-state flux distributions in metabolic models remains limited to small models due to the explosion of possibilities. Often it is sufficient to look only at all possible overall conversions a cell can catalyze ignoring the details of intracellular metabolism. Such a characterization is achieved by elementary conversion modes (ECMs), which can be conveniently computed with ecmtool. However, currently, ecmtool is memory intensive, and it cannot be aided appreciably by parallelization. Results: We integrate mplrs - a scalable parallel vertex enumeration method - into ecmtool. This speeds up computation, drastically reduces memory requirements and enables ecmtool's use in standard and high-performance computing environments. We show the new capabilities by enumerating all feasible ECMs of the near-complete metabolic model of the minimal cell JCVI-syn3.0. Despite the cell's minimal character, the model gives rise to 4.2×109 ECMs and still contains several redundant sub-networks.