In silico assessment of cell‐free systems

Abstract

Cell-free extract (CFX)-derived biocatalytic systems are usually embedded in a complex metabolic network, which makes chemical insulation of the production system necessary by removing enzymatic connections. While insulation can be performed by different methods, the identification of potentially disturbing reactions can become a rather lengthy undertaking requiring extensive experimental analysis and literature review. Therefore, a tool for network topology analysis in cell-free systems was developed based on genome scale metabolic models. Genome scale metabolic models define a potential network topology for living cells, and can be adapted to the characteristics of cell-free systems by: (i) removal of compartmentalization, (ii) application of different objective functions, (iii) enabling the accumulation of all metabolites, (iv) applying different constraints for substrate supply, and (v) constraining the reaction space through cofactor availability, microarray data, feasible reaction rates, and thermodynamics. The resulting computational tool successfully predicted for Escherichia coli-derived CFXs a previously identified undesired pathway for dihydroxyacetone phosphate (DHAP) production from adenosine phosphates. The tool was then applied to the identification of potentially interfering pathways to further insulate a DHAP-producing multi-enzyme system based on CFX.