Current Calls

Here you will find an overview of current offers for bachelor and master theses in the interdisciplinary field of synthetic biology.

  • Beschreibung des Projekts – Project Description

    Chloroplast biotechnology is mainly based on biolistic transformation and afterwards tedious regeneration of homoplasmic plants carrying transgenic chloroplasts. Unfortunately, this time-consuming creation of results easily takes months, creating a detrimental bottleneck during research purposes. Coupled transcription and translation in cell-free chloroplast extract could be a suitable way to shorten this process as a means of prototyping constructs for proper processing before biolistic transformation. Building upon the work of Clark et al. (unpublished) and the project of the Marburg 2021 iGEM project, a working cell-free chloroplast extract from Cannabis sativa and Nicotiana tabacum shall be created.

    Aufgabenstellung – Task

    During the thesis, you will be isolating chloroplasts using two different protocols. The fitness of the chloroplasts shall be compared between the two isolation protocols. Afterwards, the cell-free extract is created from the isolated chloroplasts and by means of NanoLuc luciferase assay tested for activity. In parallel, heterologous NanoLuc luciferase is produced and isolated from Nicotiana benthamiana as a control for the assay. Notable methods include Agrobacterium mediated gene transfer, Northern and Western Blotting, chloroplast isolation, in-vitro transcription and translation, etc. Vorkenntnisse – Previous knowledge Not necessary, but a profound interest in plant biotechnology and metabolic engineering is strongly encouraged! Previous knowledge in cell-free transcription and translation is advantageous.

    Zeitraum – Timeframe

    6 months, earliest starting date 3rd of November 2022. 9 months when the research internship shall be carried out in advance.

    Arbeitsgruppe und Ansprechpartner

    Kontakt über Prof. Heribert Warzecha,

    Supervisor: Prof. Dr. Heribert Warzecha

    Announcement as PDF

  • RNA plays an important role in both the transcription and translation processes. Unlike bacteria, in eukaryotes, RNA will be transported, localized, and locally translated. Therefore, it is meaningful to detect the localization and quantify the RNA. RNA-FISH is one of the most popular methods to quantify and localize RNA in fixed cells. For this reason, we would like to establish the RNA detection in Saccharomyces cerevisiae using fluorescence in situ hybridization (FISH).

    Supervisor: Prof. Dr. Heinz Koeppl

    Announcement as PDF

  • 2021/08/29

    The aim of the research project is to use deep neural networks to identify patterns in the gene sequences of homologous proteins that are responsible for successful expression. Based on these patterns, a prediction of the producibility of heterologous proteins will be made using their DNA sequence and, if necessary, verified with available experimental data.

    Supervisor: Prof. Dr. Heinz Koeppl

    Announcement as PDF

  • The laboratory of Viktor Stein takes a protein-centric approach to synthetic biology as we devise systematic approaches to engineer artificial sensory, signalling and transport functions focussing on the construction of protein switches, optical sensors, protein nanopores and membrane transporters. Strategically, we address fundamental questions exploring the design principles of artificially engineered proteins and develop them towards distinct biotechnological applications. Our work also entails a strong focus on the development and application of enabling technologies. This includes new DNA assembly methods (e.g. iFLinkC2) to assemble protein switches and sensors via combinatorial linker libraries and genetic screening systems (e.g. FuN Screen3) to study and engineer transport processes across microbial membranes. These high-throughput approaches are complemented by high-resolution analytical methods (e.g. electrophysiological measurements in lipid bilayers3 and live cell fluorescence microscopy in microfluidics3) to better understand the molecular features that underlie the function of artificially engineered proteins.

    Supervisor: Prof. Dr. Viktor Stein

    Announcement as PDF

  • Relaxed continuous time Markov chains

    Masterthesis, Bachelorthesis, undergraduate assistent


    Deep generative models such as the variational autoencoder have led to breakthroughs in generating synthetic data from complicated distributions, e.g. natural images. The key ingredient of this success story is a new way of parameter learning via sampling-based variational inference.

    Supervisor: Christian Wildner

    Announcement as PDF