OptFlux

OptFlux performs in silico metabolic engineering and phenotype simulation to identify genetic modification targets and predict metabolic behavior of microbial networks.

Key Features:

• Strain Optimization: Integration of strain optimization methods using Evolutionary Algorithms, Simulated Annealing metaheuristics, and the OptKnock algorithm to identify metabolic engineering targets.
• Phenotype Simulation: Simulation of wild-type and mutant phenotypes using Flux Balance Analysis (FBA), Minimization of Metabolic Adjustment, and Regulatory on/off Minimization of Metabolic flux changes.
• Metabolic Flux Analysis: Computation of admissible flux spaces based on sets of measured fluxes to analyze feasible metabolic states.
• Pathway Analysis: Calculation of Elementary Flux Modes (EFMs) to characterize pathway functionality within metabolic networks.
• Model Simplification and Pre-processing: Methods for model simplification and pre-processing aimed at reducing the search space for optimization algorithms.
• Data Import/Export Compatibility: Support for a range of flat file formats and compatibility with the Systems Biology Markup Language (SBML) standard.
• Visualization Module: Integration of layout information from Cell Designer to superimpose simulation results on model graphs.
• Extensibility through Plug-ins: Plug-in architecture enabling extension with network topology analysis tools and integration of Boolean network-based regulatory models.

Scientific Applications:

• Strain design for production: Identification of genetic modifications to improve microbial production of target compounds.
• Phenotype prediction: Prediction of metabolic responses of wild-type and mutant strains under genetic and environmental perturbations.
• Flux-based analysis: Integration of measured fluxes to delineate admissible flux spaces and support metabolic flux analysis.
• Pathway and network analysis: Use of EFMs and network topology analyses to identify pathway bottlenecks and alternative routes.

Methodology:

Computational methods explicitly include Evolutionary Algorithms, Simulated Annealing metaheuristics, OptKnock, Flux Balance Analysis (FBA), Minimization of Metabolic Adjustment, Regulatory on/off Minimization of Metabolic flux changes, computation of admissible flux spaces from measured fluxes, Elementary Flux Modes calculation, model simplification and pre-processing, SBML compatibility, Cell Designer layout integration, and a plug-in architecture supporting Boolean network-based regulatory models.