MARSI

MARSI identifies target metabolites and predicts the effects of metabolite analogues using genome-scale metabolic models to guide rational strain improvement in industrial microorganisms.

Key Features:

• Metabolite analogue competitive inhibition: Models metabolite analogues (MAs) that are structurally similar to native metabolites as competitive inhibitors of specific enzymatic reactions.
• Targeted metabolite selection: Identifies potential target metabolites within an organism's metabolic network rather than focusing solely on genes or reactions.
• Genome-scale metabolic modeling: Uses genome-scale metabolic models to predict how MAs will influence cellular phenotypes and production traits.
• Selection-based non-GMO approach: Leverages MA-driven inhibition as a selection mechanism to isolate mutants and achieve metabolic rewiring without recombinant DNA methods.
• Database integration for candidate MAs: Searches an extensive database of known drugs and analogues to identify candidate metabolite analogues for specified target metabolites.

Scientific Applications:

• Industrial strain optimization: Optimize microbial strains for production of pharmaceuticals, biofuels, and other valuable biochemicals by guiding selection strategies with MA predictions.
• Non-GMO metabolic engineering: Enable metabolic rewiring and mutant selection without recombinant DNA to support workflows constrained by regulatory frameworks.
• Experimental design and selection: Inform selection of MAs and anticipated phenotypic outcomes to streamline isolation of desirable mutants.

Methodology:

Identifies target metabolites within a metabolic network, applies genome-scale metabolic models to predict MA-induced phenotypic effects, and queries a database of known drugs and analogues to find candidate MAs.