Research Quantitative Microbial Phenotyping
High-Throughput Bioprocess Characterization
Characterization of microbial bioprocesses at higher throughput is an increasingly important part in modern biotechnology. This incorporates on the one hand fast screening of potential producer strains on the basis of product titers and productivities. On the other hand, candidate strains are further investigated and optimized by testing large parameter spaces with respect to media composition, induction profiles and process control.
We perform high-throughput bioprocess characterization by combining microtiter plate cultivation, lab automation, microbial strain libraries and biological fluorescence markers. The resulting datasets are complementary to cultivation data stemming from classical pilot-scale bioreactor experiments (liter scale) as well novel microfluidic chip experiments (picoliter scale) performed in the Micro Scale Bioengineering Group[1,2].
The work is closely connected to several groups of the Systemic Microbiology providing interesting candidate strains for in-depth quantitative phenotyping .
1 Rohe P, Venkanna D, Kleine B, Freudl R, Oldiges M. (2012) An automated workflow for enhancing microbial bioprocess optimization on a novel microbioreactor platform. Microb Cell Fact http://dx.doi.org/10.1186/1475-2859-11-144
2 Grünberger A, van Ooyen J, Paczia N, Rohe P, Schiendzielorz G, Eggeling L, Wiechert W, Kohlheyer D, Noack S. (2012) Beyond growth rate 0.6: Corynebacterium glutamicum cultivated in highly diluted environments.
Biotechnol Bioeng http://dx.doi.org/10.1002/bit.24616
3 van Ooyen J, Noack S, Bott M, Reth A, Eggeling L. (2012) Improved L-lysine production with Corynebacterium glutamicum and systemic insight into citrate synthase flux and activity. Biotechnol Bioeng http://dx.doi.org/10.1002/bit.24486
Next Generation Omics-Technologies
Quantitative omics-technologies play a key role in driving systems biology to an applied science for metabolic engineering and synthetic biology of microorganisms. Providing quantitative data of the cell’s transcriptome, proteome, metabolome and fluxome opens the opportunity to piecewise unravel the complex regulatory mechanisms underlying all in vivo metabolic processes.
However, for quantitative analyses not only experimental data, but also their measurement errors play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps.
We focus on the development and optimization of methods for the quantification of intracellular proteins, metabolites and fluxes based on thoroughly analyzing the propagation of all errors during sample processing [1,2].
Resulting multi-omics-data are exchanged with the Modeling and Simulation Group for the integrative analysis applying sophisticated vertical modeling approaches .
2 Voges R, Noack S. (2012) Quantification of proteome dynamics in Corynebacterium glutamicum by 15N-labeling and selected reaction monitoring. J Proteomics http://dx.doi.org/10.1016/j.jprot.2012.03.020
3 Wiechert W, Noack S (2011) Mechanistic Pathway Modeling for Industrial Biotechnology: Challenging but worthwhile. Curr Opin Biotechnol http://dx.doi.org/10.1016/j.copbio.2011.01.001