Automation on the Generation of Genome-Scale Metabolic 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, Synechocystis sp. * daniel.gamermann@ucv.es 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.

Rapid construction of metabolic models for a family of Cyanobacteria using a multiple source annotation workflow

“…There are a number of approaches for the automated development of metabolic reconstructions [35,62-64] affording significant gains in development time, however, at the expense of some omissions and erroneous additions. The Cyanothece models created using the MIRAGE method contain generalized lipids along with a non-specific acceptor metabolite [64].…”

“…The model produced through KBase also does not contain the pigment β-carotene. Many of these models do not have specified compartments apart from cytoplasm and extracellular space [35,62,64]. Automated model development can exclude unique metabolic pathways if they are absent from the training set of models.…”

Genome-scale modeling for metabolic engineering

“…The SuBliMinaL Toolbox [93] is a framework for reconstructing metabolic networks by providing independent modules that can be used individually or in a pipeline, and can perform tasks that are common in every reconstruction process, such as generating a draft, determining metabolite protonation states, mass-balancing reactions, compartmentalizing the cell, adding transport reactions, creating a biomass function and exporting the reconstruction in a format readable by software packages (typically SBML). Reyes et al [77] presented an automatic method for the reconstruction of genome-scale metabolic models for any organism implemented in COPABI. Dale et al [23] developed a method for predicting metabolic pathways that relies on machine learning approaches to reconstruct the network of an organism.…”

Modelling and multiobjective optimization for simulation of cyanobacterial metabolism

“…For the first automated assembly two software were applied, Pathway Tools (Karp et al, 2002 and COPABI (Reyes et al, 2012), which allowed double checking the resulting automated reconstructions. Specific functions for gap filling and duplicate check from COPABI were applied.…”

“…After this first stage of automated generation of components, a draft reconstruction was obtained that accounted for 540 enzymes encoded by 672 genes, and included 898 reactions. Using the gap filling function, incomplete pathways were completed based on probabilistic criteria of unicity and completeness (for details of this process please check reference Reyes et al (2012)). …”