Lab robotics for synthetic biology and strain evaluation (RoboSyn)

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The lab robotic RoboSyn should be used for synthetic biology and systems biology research at the University of Stuttgart, in particular at the ‘Institut für Bioverfahrnstechnik (IBVT). The device should support the key research activities (A) strain- and process engineering, (B) cell-free protein synthesis, and (C) RNA-RNA interaction. By establishing RoboSyn the current gap in lab automation and in parallel strain testing in mL and mikroL scale will be closed. RoboSyn is designed such that in vitro and in vivo tests can be performed successfully. The technology builds on the expertise of the applicants and on the knowhow established in Stuttgart. RoboSyn complements existing infrastructure by giving access to lab automation which is missing so far.Regarding (A) RoboSyn will be used for the massively parallel evaluation of novel strains constructed in multiple projects. Besides, modern approaches of generating coding DNA from DNA fragments and their integration in microbial hosts will be automatized. Studies will comprise the optimization of expression levels of individual genes (e.g. in biosynthetic pathways) via cell lysate tests, too. The question is linked to the topics of (B), the cell free protein synthesis. Based on profound expertise of preliminary studies, novel cells and expression hosts will be targeted. Detailed systems biology studies will elucidate their functioning and their suitability for the production of pharmaceutically and medically attractive compounds. Furthermore, RoboSyn will be used to elucidate RNA-RNA mediated regulatory networks in pro- and eukaryotic cells (C). Such systems biology studies will benefit from the possibility of installing distinct perturbations followed by ‘global’ monitoring in the parallel read-out system.In agreement with the leitmotif of IBVT, all studies will make use of mathematical models either to check experimental data or to design experiments. As such, studies will be structured as complete design-build-test-learn cycles. The latter is of particular importance as it ensures a knowledge-based optimization of strains and processes.