Automation on the generation of genome scale metabolic models

TL;DR

This paper introduces COPABI, an automated computational platform that reconstructs genome-scale metabolic models for various organisms using probabilistic algorithms, significantly reducing manual effort and time.

Contribution

It presents an automated methodology and platform for genome-scale metabolic model reconstruction, replacing manual, time-consuming processes with probabilistic algorithms.

Findings

Models generated by COPABI match manually curated models in network properties.

The platform successfully reconstructs models for multiple organisms.

Validation shows robustness and consistency with existing models.

Abstract

Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.