The KIWI Biolab focuses on automated methods and model-based bioprocess engineering. Our main goal is to find optimal process conditions for biotechnological applications. For this purpose, pipetting robots / liquid handling stations are combined with process models and machine learning approaches. By combining automation and modeling, highly complex tasks can be performed quickly and precisely that would not be possible manually.

The KIWI Biolab is dedicated to a broad spectrum of biotechnological process development from library screening to conditional screening under scale-down conditions. The development and application of mechanistic models represents a large part of the research group. In the context of collaborations, hybrid models and machine learning are increasingly becoming the focus of our work. The developed models are used for process monitoring as well as for process control (model predictive control). In addition to the model-based methods, the HT lab is the heart of this research group. With the establishment of this lab, the KIWI Biolab has acquired great competences in automation, integration of lab equipment and software engineering. This knowledge is contributed to various projects and to the SiLA consortium.

The KIWI biolab is headed by Dr. Mariano Nicolas Cruz Bournazou.

He holds a bachelor degree in Chemical Engineering and a PhD in the field of Model Based Optimization of Bioprocesses from the TU Berlin. His international experience includes research stays at Texas A&M (process control laboratory, Prof. Kravaris) and ETH Zurich (Morbidelli group). The research of Dr. Cruz is focused in the fields of bioprocess digitalization, model-based tools for biotechnology and biopharma, High Throughput Bioprocess Development, and autonomous biolabs. In recent years, his research has pushed the integration of model-based methods and High Throughput experiments to accelerate bioprocess development. The most relevant achievements include the first adaptive algorithms for online optimal redesign of parallel experiments and novel hybrid (Machine Learning and dynamical modelling) tools for bioprocess engineering. The driving force of his research is the conviction that robotic systems need proper digital tools, models, and algorithms to fully exploit its capabilities in bioprocess development and biomanufacturing.