Project descriptions

The IRTG 2466 "Network, exchange, and training program to understand plant resource allocation" invites applications for new PhD projects

Information about application requirements and the online application form can be found under undefined"Application & Finances"

Applicants holding an excellent Bachelors degree are particularly encouraged to apply.

Application deadline: November 10, 2019




Open Projects


Projects in Biology and related fields

Project ID: A4

Project title: Quantitative understanding of plant light and hormone signaling through synthetic reconstruction in orthogonal mammalian cell systems

Supervising PI: Matias Zurbriggen

Research project: Synthetic biology strategies provide alternative theoretical-experimental resources that revolutionize the way we can study biological systems, overcoming current limitations and yielding quantitative assets. However, their implementation in plant biology lags behind. In this project, we will implement a synthetic biology approach to obtain a quantitative understanding of mechanistic and regulatory principles involved in light and hormone signaling networks leading to decisions on growth and development. Current genetic and biochemical approaches provide a complete description of the signaling cascades in terms of components, connectivity, and function. However, a thorough quantitative understanding is precluded by the combinatorial genetic complexity and multifactorial dynamic interactions posing experimental constraints. We will here perform a partial reconstruction of light and hormone signalling networks in a heterologous mammalian cell system, thereby simplifying the protein environment, limiting redundancy, and avoiding interactions with endogenous components that affect the analysis in planta. The implementation of numerous synthetic biology tools, reporters and readout system, and advanced microscopy techniques available in mammalian cells will yield quantitative data which will be mathematically modelled to obtain a structural and functional description of the networks. Finally, a targeted validation of regulatory networks structure and function will be performed in plant cells. 


  • Beyer, H.M., Juillot, S., Herbst, K., Samodelov, S.L., Muller, K., Schamel, W.W., Romer, W., Schafer, E., Nagy, F., Strahle, U., Weber, W., and Zurbriggen, M.D. (2015). Red Light- Regulated Reversible Nuclear Localization of Proteins in Mammalian Cells and Zebrafish. ACS synthetic biology 4, 951-958.
  • Kolar, K., Wischhusen, H.M., Muller, K., Karlsson, M., Weber, W., and Zurbriggen, M.D. (2015). A synthetic mammalian network to compute population borders based on engineered reciprocal cell-cell communication. BMC Syst. Biol. 9, 97.
  • Samodelov, S.L., and Zurbriggen, M.D. (2017). Quantitatively Understanding Plant Signaling: Novel Theoretical-Experimental Approaches. Trends Plant Sci 22, 685-704.
  • Samodelov SL, Beyer HM, Guo X, Augustin M, Jia K-P, Beyer P, weber W, Al-Babili S, Zurbriggen MD (2016) StrigoQuant: a genetically encoded biosensor for quantifying strigolactone activity and specificity. Science Advances 2:e1601266
  • Stahl, Y., Grabowski, S., Bleckmann, A., Kuhnemuth, R., Weidtkamp-Peters, S., Pinto, K.G., Kirschner, G.K., Schmid, J.B., Wink, R.H., Hulsewede, A., Felekyan, S., Seidel, C.A., and Simon, R. (2013).
  • Wend, S., Dal Bosco, C., Kampf, M.M., Ren, F., Palme, K., Weber, W., Dovzhenko, A., and Zurbriggen, M.D. (2013). A quantitative ratiometric sensor for time-resolved analysis of auxin dynamics. Scientific reports 3, 2052.

Project ID: C2.2

Project title: Iron regulation modules in the context of Fe resource allocation, seed sink strength and evolution in Arabidopsis thaliana

Supervising PI: Petra Bauer

Research project: Iron (Fe) is an essential metal for numerous electron transfer reactions in plants and thereby affects drastically photosynthetic activities, carbon sinks, plant growth and yield. Fe plays a vital role in oxygen transport in humans and is critical for quality nutrition. In angiosperms, Fe is mobilized and taken up by the root, subsequently allocated to different plant parts and cellular compartments and delivered to sinks like seeds and grains. This process is controlled by an Fe-regulated transcription factor (TF) cascade. Fe-regulated TFs target different clusters of co-expressed genes, that encode regulatory proteins of the cascade itself as well as metal homeostasis components, acting locally and long-distance in metal uptake from the soil and internal Fe resource allocation. Here, we will study mechanistic details how Fe resource allocation is triggered by the Fe regulation cascade and sink strength and the evolution of the Fe regulation modules in seed plants. Techniques of genetics, molecular biology, plant physiology, synthetic biology and computational analysis will be applied.


  • Naranjo Arcos M.A., Maurer F., Meiser J., Pateyron S., Fink-Straube C., Bauer P. (2017) Dissection of iron signaling and iron accumulation by overexpression of subgroup Ib bHLH039 protein, Sci. Rep. 7, 10911, DOI: 10.1038/s41598-017-11171-7.
  • Mai H.J., Pateyron S., Bauer P. (2016) Iron homeostasis in Arabidopsis thaliana: transcriptomic analyses reveal novel FIT-regulated genes, iron deficiency marker genes and functional gene networks. BMC Plant Biol. 16, 211, DOI: 10.1186/s12870-016-0899-9
  • Ivanov, R., Brumbarova, T., and Bauer, P. (2012). Fitting into the harsh reality: Regulation of iron deficiency responses in dicotyledonous plants. Mol. Plant 5, 27-42,  DOI: 10.1093/mp/ssr065.

undefinedOfficial HHU advertisement


Projects in Bioinformatics, Mathematics and related fields

Project ID: A3

Project title: Predictive Genotype-Phenotype Mapping using Deep Learning

Supervising PI: Markus Kollmann

Research project: Advances in Machine Learning – in particular the use of Deep Neural Network – has led to unprecedented improvements on image classification tasks, superresolution, image generation, and predicting protein structure from multiple sequence alignments. In this project we will employ deep neural networks to predict how the life style of bacteria affects the use of synonymous codons. We expect to find differences in codon usage patterns especially between fast growing bacteria and bacteria that have been selected for efficient carbon metabolism. We are in particular interested how these patterns are related to pathway organization patterns. As the codon usage bias is strongly related to translational efficiency, the task is to predict the latter, which further involves accurate prediction of RNA secondary structures. Our approach makes use of autoregressive neural networks in combination with MCMC sampling methods to explore the likelihood function predicted from the data (~300M bacterial genomes). The predictions on translational efficiency and RNA folding will be validated by experiments carried out by the Axmann Group.

Required skills and qualifications: The successful candidate must have strong computational (python) and analytical skills, strong interest in life science problems, and be willing to dig deep into the deep learning literature. 

Project ID B2.2

Project title: A non-linear model for the growth of photosynthetic cells

Supervising PI: Martin Lercher

Research projectNatural selection has likely optimized many microbial cells for fast growth, including those of cyanobacteria and unicellular algae. Accordingly, to simulate cellular growth and basic physiology of photosynthetic organisms, one can formulate computer models that capture the important physical and biochemical constraints under which their growth occurs and maximize the growth rate in silico. This idea forms the basis of popular genome-scale modeling methods such as flux balance analysis (FBA), but also of more advanced methods such as resource balance analysis (RBA) and metabolism and expression (ME) models. However, all these methods ignore the concentrations of metabolites, which have been shown experimentally to strongly influence cellular performance. Our group has developed a modeling scheme called growth balance analysis (GBA) that explicitly accounts for metabolite concentrations (Dourado & Lercher 2019,; see also Dourado & Lercher 2017, This scheme has not yet been applied to models of photosynthetic organisms.

In the proposed project, the PhD student will develop such models. We will start from available FBA models of cyanobacteria, which we will first reduce to a coarse-grained core model with lumped reactions. We will then integrate them with kinetic rate laws for the photosynthetic and metabolic reactions and a model of protein translation. Particular challenges will be the parameterisation of the reactions by comparison with experimental data obtained from literature sources and from collaborators at HHU, and the computational solution of the large non-linear optimisation problem. We aim to build a model that will not only allow us to understand the condition dependence of resource allocation in photosynthetic cells, but that will provide an accurate model to aid in the design of synthetic cells. 

Required skills and qualifications: Applicants for this project should show an interest in mathematical modelling and have a first degree in biology, physics, computer science, or a related subject.






iGRAD-Plant Coordinator

Dr. Petra Fackendahl

Institute of Plant Biochemistry
Universitätsstr. 1
Gebäude: 26.13.
Etage/Raum: U1.78
40225 Düsseldorf
Tel.: +49 211 81-10588


Mo-Do 9.00-17.00

Verantwortlich für den Inhalt: E-Mail sendenDr. Petra Fackendahl