As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

SUMMARY: Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. AVAILABILITY AND IMPLEMENTATION: MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.

BACKGROUND: As genome sequencing projects grow rapidly, the diversity of organisms with recently assembled genome sequences peaks at an unprecedented scale, thereby highlighting the need to make gene functional annotations fast and efficient. However, the (high) quality of such annotations must be guaranteed, as this is the first indicator of the genomic potential of every organism. Automatic procedures help accelerating the annotation process, though decreasing the confidence and reliability of the outcomes. Manually curating a genome-wide annotation of genes, enzymes and transporter proteins function is a highly time-consuming, tedious and impractical task, even for the most proficient curator. Hence, a semi-automated procedure, which balances the two approaches, will increase the reliability of the annotation, while speeding up the process. In fact, a prior analysis of the annotation algorithm may leverage its performance, by manipulating its parameters, hastening the downstream processing and the manual curation of assigning functions to genes encoding proteins. RESULTS: Here SamPler, a novel strategy to select parameters for gene functional annotation routines is presented. This semi-automated method is based on the manual curation of a randomly selected set of genes/proteins. Then, in a multi-dimensional array, this sample is used to assess the automatic annotations for all possible combinations of the algorithm's parameters. These assessments allow creating an array of confusion matrices, for which several metrics are calculated (accuracy, precision and negative predictive value) and used to reach optimal values for the parameters. CONCLUSIONS: The potential of this methodology is demonstrated with four genome functional annotations performed in merlin, an in-house user-friendly computational framework for genome-scale metabolic annotation and model reconstruction. For that, SamPler was implemented as a new plugin for the merlin tool.