ASTHERISC

ASTHERISC optimizes thermodynamic driving force for product synthesis by algorithmically searching stoichiometric single-species community models to identify pathway partitions across strains that enhance bio-based chemical production.

Key Features:

• Thermodynamic optimization: Maximizes the thermodynamic driving force for product synthesis by configuring pathway distribution among strains.
• Pathway partitioning: Divides metabolic pathways across different single-species strains to reduce metabolic burden and exploit compartmentalization.
• Thermodynamic bottleneck identification: Detects reactions that require incompatible metabolite concentrations and proposes compartmentalization to circumvent these bottlenecks.
• Algorithmic search within stoichiometric models: Performs an algorithmic search to determine optimal multi-strain community configurations using stoichiometric models.
• Model compatibility and testing: Implemented and tested on core and genome-scale metabolic models of Escherichia coli.
• Scenario analysis: Evaluates configurations across varying numbers of strains and product yield requirements.
• Enables multi-strain high-yield solutions: Shows that many target metabolites gain thermodynamic advantages in multi-strain communities and that some high-yield productions are feasible only via consortia.

Scientific Applications:

• Synthetic microbial consortia design: Design of single-species strain communities for enhanced bio-based chemical production using thermodynamic criteria.
• Metabolic engineering: Identification of pathway splits to improve thermodynamic feasibility and yields of target metabolites.
• Bioprocess optimization: Determination of when multi-strain communities are required to achieve specified product yields.
• E. coli model analysis: Benchmarking and hypothesis generation using core and genome-scale Escherichia coli metabolic models.

Methodology:

Performs an algorithmic search within stoichiometric models to determine optimal multi-strain configurations and was evaluated using core and genome-scale metabolic models of Escherichia coli across scenarios with varying strain numbers and product yield requirements.