lunedì 4 luglio 2016, ore 14:00, aula 2025, edificio U3,
Dipartimento di Biotecnologie e Bioscienze, Università degli Studi Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy
Prof. Dr.-Ing. Peter Goetz – Beuth University of Applied Sciences Berlin, Bioprocess Engineering, Seestrasse 64, 13347 Berlin, Germany
The field of applied systems biotechnology comprises experimental work as well as mathematical modeling. Aimed at industrial application, the resulting multi-scale nature of this approach, from molecule to production plant, allows and requires the integration of many research disciplines. In our work, we focus on the interaction between microbial metabolism and bioreactor environment. From genome sequencing and metabolic network reconstruction, capabilities of the investigated microorganism are available. Data on transcriptome, proteome and metabolome characterize the current physiological state of the cells in an experiment. Combining the network structure and the experimental data, a quantitative description of the biochemical reaction network within the cells will lead to a predictive mathematical model, the in silico cell. Coupling this model to a model for the cellular environment, the bioreactor conditions, will finally allow process optimization and advanced process control.
Butanol production by Clostridium acetobutylicum is a promising process to replace fossil resources of chemicals and fuel. Solvent producing Clostridia exhibit a peculiar behavior during a batch process. An acidogenic growth phase, where biomass and organic acids are produced, is followed by a solventogenic phase, where the acids are converted into solvents and sporulation is initiated. To make this process economically feasible, molecular biology (e.g. overexpression of solvent producing pathway enzymes) as well as process engineering (e.g. optimizing bioreactor conditions) must contribute to this joint effort. Between the disciplines, a cyclic workflow of experiments, evaluation/modeling and hypothesis generation, followed by new experiments, is established. This leads to a refined quantitative description, which helps to understand dynamic features of this process and will allow optimal process design.
Ospite: Paola Branduardi