meneco

meneco performs topological gap-filling of genome-scale draft metabolic networks to identify missing biochemical reactions and infer metabolic capabilities from incomplete or poorly annotated sequence-derived reconstructions.

Key Features:

• Topological gap-filling: Targets genome-scale draft metabolic networks to identify and propose missing reactions required to complete metabolic pathways.
• Qualitative combinatorial optimization: Reformulates the gap-filling problem as a qualitative combinatorial optimization task rather than a stoichiometry-based optimization.
• Stoichiometry and cofactor omission: Omits stoichiometric balance and explicit cofactor constraints to operate when phenotypic, taxonomic, or stoichiometric data are unavailable or unreliable.
• Answer Set Programming (ASP): Employs ASP, a declarative programming approach, to solve the combinatorial search problem.
• Scalability and benchmarking: Demonstrated ability to identify essential missing reactions in highly degraded networks and was validated on artificial datasets comprising 10,800 degraded Escherichia coli networks, showing scalability compared with stoichiometry-based tools.
• Data compatibility: Applicable to reconstructions derived from sequence data and has been used with transcriptomic and metabolomic datasets in case studies.

Scientific Applications:

• Draft metabolic network completion: Completing genome-scale metabolic reconstructions derived from incomplete or poorly annotated sequence data.
• Identification of essential reactions in degraded networks: Detecting core missing reactions in highly degraded Escherichia coli networks for benchmarking and analysis.
• Inter-organism metabolic interaction inference: Identifying candidate metabolic pathways that could facilitate interactions between the brown alga Ectocarpus siliculosus and the bacterium Candidatus Phaeomarinobacter ectocarpi.
• Non-model organism reconstruction: Reconstructing the first metabolic network for the microalga Euglena mutabilis using transcriptomic and metabolomic data.

Methodology:

Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omits stoichiometric and cofactor constraints, and solves the problem using Answer Set Programming (ASP).