(191dj) Predicting Metabolic Disruptions Due to Heterologous Pathway Expression

Synthetic biology and metabolic engineering have been instrumental in transforming organisms such as E. coli and yeast to microbial factories to produce a wide range of commercially useful biomolecules by introducing non-native synthesis pathways in microbial hosts. While the general consensus has been, that enzymes are highly selective biocatalysts, recent evidences suggest that enzymes are often promiscuous. We hypothesize that such promiscuity in heterologous enzymes could promote metabolic disruptions in the expression host by catalyzing unwanted or unexpected reactions. Current computational synthesis methods like PathPred and ProPath can identify pathway(s) to direct host metabolites to desired end-products but cannot predict metabolic disruptions incurred by overexpression of recombinant pathways. In this work, we developed a computational method to assess the disruptive impact of synthesis pathways to microbial hosts. The steps in this predictive algorithm are as follows. First, we use PROXIMAL, an algorithm that predicts structural modifications to reactants by enzymes, generating potential byproducts that could be synthesized due to promiscuous activities of the heterologous enzymes. Second, generated products and associated reactions are incorporated into the underlying host network model as new nodes and edges. Third, yield is evaluated based on the augmented host. We quantify the metabolic disruption introduced as a change in maximum attainable product yield. We also explored the correlation of disruption with enzymatic choices and network topology. This work is novel as it is the first to examine the question of enzyme promiscuity in relationship to synthesis pathways.