Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

Abstract

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce , a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Reconstruction of 24 Penicillium genome‐scale metabolic models shows diversity based on their secondary metabolism

“…Once the merging was done, the resulting metabolic networks were gap‐filled using the gap‐filling tool Meneco (Prigent et al, ). A union of MetaCyc and iAL1006 was chosen as a database of reactions to enable the production of all molecules of the biomass present in iAL1006 , which corresponds to the target metabolites.…”

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

“…Second, many genes are lacking a functional annotation due to a lack of knowledge [7] and, thus, also the gene products cannot be integrated into the metabolic networks, which potentially lead to gaps in pathways. Third, the gap-filling of metabolic networks is frequently done by adding a minimum number of reactions from a reference database that facilitate growth under a chemically defined growth medium [34,63,84]. Such approaches miss further evidences potentially hidden in sequences and are biased towards the growth medium used for gap-filling.…”

Expanding Metabolic Capabilities Using Novel Pathway Designs: Computational Tools and Case Studies

“…Conversely, the reconstructed subnetwork can be inferred following a bottom‐up approach, where the annotated genes of a determined organism are individually mapped based on different criteria onto the supernetwork. The resulting subnetwork typically does not describe the observed phenotypes and metabolic functions; hence, reaction gap‐filling is usually performed to reconcile the subnetwork with the data . By minimizing the number of reaction additions or maximizing a confidence score for the added reactions, gap‐filling algorithms seek to arrive at a parsimonious subnetwork.…”