AntND: A java framework for visualization, curation and optimization of metabolic networks.

Abstract

Abstract

Metabolic modeling is a widely used tool for the study and prediction of cell metabolism. High-quality metabolic models are needed for accurate metabolic simulations and predictions. The database BioModels (July 2015) hosts 2641 whole-genome stoichiometric models created using pathway information from KEGG or MetaCyc. The curation of a whole-genome metabolic model to make it functional and able to predict experimental data is a tedious process (Thiele 2010). AntND is a software framework that enhances the manual curation, analysis, visualization and optimization of metabolic models. It contains modules for reading and writing files in SBML format, easily editing the models, extracting subnetworks based on pathway information or based on the shortest path search, performing FBA and flux optimizations, visualizing and analyzing the metabolic networks. The visualization of the models is one the most powerful modules in AntND. It allows to extract and create layouts using GML (Graph Modeling Language) format, easily expand the network and coloring the nodes.

Avaliability and implementation: AntND is implemented using java and is distributed freely under GNU General Public Licence. It’s code is hosted in github.

Introduction

Understanding the metabolism of organisms is essential in many fields such as systems biology, metabolic engineering, disease research, etc. Whole-genome metabolic models are one of the most important tools used to model mathematically the metabolism of an organism. They can be seen as a representation of interconnected chemical reactions that represent our knowledge about the metabolism of the organism.

The reconstruction of a metabolic model is done in many cases in a automatic or semi-automatic way producing in many cases incomplete and non-accurate models. After a metabolic model has been created there is a need for curating, testing and visualizing it so all the problems can be solved in a short time frame.

Visualizing a metabolic network is still a challenge because of the big number of nodes highly interconnected (the nodes represent reactions and compounds). The current visualization methods rely on the knowledge of the curator to extract specific sub-networks from the model, but the model may contain unexpected pathways or reactions wrongly added by the reconstruction software. There is a need for an automatic sub-network extraction that takes into account the constrains and characteristics of that heterogeneous network. Many visualization tools exists as a part of metabolic reconstruction software (Rocha 2010) (Dias 2015) or independent of it (Shannon 2003) where the user may visualize part of metabolic network but they are unable to extract automatically sub-networks without information of all their components.

In this work we present an algorithm that can extract a sub-network taking into account the reaction stoichiometry and its constraints. The algorithm takes as a input the initial compound o compounds where the network is starting, a f