Sven Panke
Panke, Sven
Science and Engineering ETH
Basel, Switzerland
Title: Synthetic Biology for fine chemistry: orthogonalization of metabolic modules
Authors: Matthias Bujara, Sonja Billerbeck, Michael Schümperli, Rene Pellaux, Christoph Hold, Sven Panke.
Abstract:Biotechnology plays an increasingly central part in the manufacturing of compounds in the pharmaceutical, chemical, and fuel industry. The underlying biological research has moved beyond the molecular reductionist dogma to a systems view, and novel system-wide analytic tools allow unprecedented insight into the relevant processes in cells. At the same time, metagenomics increases drastically the gene pool from which to recruit catalysts. The ETHZ Bioprocess Laboratory concentrates on the rational engineering of in vitro multi-enzyme reaction networks, in particular for the production of natural and unnatural sugars and ultimately oligosaccharides. Crucial questions are how to insulate efficient pathways from highly interconnected networks, such as the central carbon metabolism, and how to optimize these pathways in terms of dynamic behavior.

The former point is addressed by pre-engineering cellular enzymatic networks such that the desired reaction networks in the resulting cell free extracts become more and more insulated. By removing small molecules from the system, this insulation can be advanced already rather far, but problems with enzymes essential for growth remain, in addition to other enzymes that interfere with the insulation of either the metabolic network or additional functions such as cofactor regeneration. These problems can be addressed by developing strategies to selectively eliminate enzymes from a cell free extract after production, such as selective proteolytic degradation.

In order to address the latter point, it is imperative to implement the right analytical tools to study the time-behavior of the molecular network in sufficient detail. We have worked towards a real-time MS-method that allows tracking the effect of perturbations to the network in high data-density and frequency. This in turn allows on-line optimization of enzyme networks, such as the upper part of glycolysis.

Questions & Comments: