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Esfandabadi M.E.,Institute of Biochemical Engineering | Wucherpfennig T.,Institute of Biochemical Engineering | Krull R.,Institute of Biochemical Engineering
Journal of Chemical Engineering of Japan

One of the most frequently used microorganisms in industrial bioprocesses is the filamentous fungus Aspergillus niger with not easily controllable morphology, ranging from dense spherical pellets to viscous mycelia depending on culture conditions. The main parameter which influences the morphology is the mechanical stress induced by either stirring or aeration. The well-established computational fluid dynamics (CFD) facilitates the quantification of the stress due to turbulent fluctuations, namely the Reynolds stress, and characterization of the flow pattern throughout the reactor by using appropriate turbulence models (Reynolds Stress Model; RSM). In order to refer the numerical simulation to the cultivation process in a multi-phase stirred tank bioreactor (STBR), a parallel research has been undertaken concerning the distinct pellet morphology of A. niger. The characterization of Reynolds stresses is based on the magnitude and the direction of tensor elements. Using CFD delivers the so-called hot spots in the reactor with respect to positioning and magnitude of various stress tensor components, respective velocity of phases and kinetic energy dissipation. For instance, in this case, the discharge zones of the air sparger and the two impellers are the regions in which cells are prone to deform or be damaged. Furthermore, the normal stress can cause more cell damage and possibly cell comminution. © 2012 The Society of Chemical Engineers, Japan. Source

Fang M.,Institute of Biochemical Engineering | Wang T.,Institute of Biochemical Engineering | Zhang C.,Institute of Biochemical Engineering | Bai J.,Institute of Biochemical Engineering | And 4 more authors.
Metabolic Engineering

Because high-throughput screening tools are typically unavailable when using the pathway-engineering approach, we developed a new strategy, named intermediate sensor-assisted push-pull strategy, which enables sequential pathway optimization by incorporating a biosensor targeting a key pathway intermediate. As proof of concept, we constructed an l-Trp biosensor and used it to optimize the deoxyviolacein biosynthetic pathway, which we divided into two modules with l-Trp being the product of the upstream and the substrate of the downstream module for deoxyviolacein synthesis. Using the biosensor and fluorescence-activated cell sorting, the activities of the two modules were sequentially and independently optimized in Escherichia coli to achieve the desired phenotypes. By this means, we increased the deoxyviolacein titer 4.4-fold (1.92. g/L), which represents the greatest deoxyviolacein production reported. This work suggests that a biosynthetic pathway can be enhanced to produce a value-added secondary metabolite(s) without available end-product screening method by using a central metabolic junction molecule biosensor(s). © 2015 International Metabolic Engineering Society. Source

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