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About

Tilman Barz is a leading researcher in bioprocess engineering, specializing in the automation and optimization of parallel cultivation systems. His work centers on integrating robotic liquid handling stations with adaptive, model-based control to accelerate bioprocess development. Barz’s major contribution is the creation of a framework for online optimal experimental re-design, which enables precise estimation of kinetic growth model parameters with minimal experimental effort—a breakthrough for rapid model validation. His most cited paper (2016, 104 citations) demonstrates this approach in robotic parallel fed-batch facilities, while subsequent work (2018, 31 citations) shows adaptive optimal operation to enhance robustness against uncertainties. Barz has also advanced automated conditional screening of *E. coli* strains and knockout mutants (2020, 16 and 4 citations), allowing early-stage host selection with limited reactor-scale information. His achievements include pioneering the use of iterative, data-driven strategies to bridge the gap between small-scale screening and industrial performance, significantly reducing development timelines. Barz’s research is pivotal for students and researchers seeking to harness automation and optimal experimental design for efficient, scalable biomanufacturing.