SBML Level 3 Package Proposal: Flux

This document describes an easy to implement package for storing information related 
to flux balance analysis of SBML Level 3 models (the FBA package). In addition, 
we provide an example of how this package may be implemented and used as a SBML
Level 2 annotation

SBML Level 3 Package: Flux Balance Constraints ('fbc')

Constraint based modeling is a widely accepted methodology used to analyze and study biological networks on both a small and whole organism (genome) scale. Typically these models are underdetermined and constraint based methods (e.g. linear, quadratic optimization) are used to optimize specific model properties. This is assumed to occur under a defined set of constraints (e.g. stoichiometric, metabolic) and bounds (e.g. thermodynamic, experimental and environmental) on the values that the solution fluxes can obtain. Perhaps the most well known (and widely used) analysis method is Flux Balance Analysis (FBA; Orth et al., 2010) which is performed on Genome Scale Reconstructions (GSR’s; Oberhardt et al., 2009). Using FBA a target flux is optimized (e.g. maximizing a flux to biomass or minimizing ATP production) while other fluxes can be bounded to simulate a selected growth environment or specific metabolic state. As constraint based models are generally underdetermined, i.e. few or none of the kinetic rate equations and related parameters are known, it is crucial that a model definition includes the ability to define optimization parameters such as objective functions, flux bounds and constraints. Currently this is not possible in the Systems Biology Markup Language (SBML) Level 2 or Level 3 core specification (Hucka et al., 2011, 2003). The question of how to encode constraint based (also referred to as steady state or FBA) models in SBML is not new. However, advances in the methods used to construct genome scale constraint based models and the wider adoption of constraint based modeling in biotechnological/medical applications have led to a rapid increase in both the number of models being constructed and the tools used to analyze them. Faced with such growth, both in number and diversity, the need for a standardized data format for the definition, exchange and annotation of constraint based models has become critical. As the core model components (e.g. species, reactions, stoichiometry) can already be efficiently described in SBML (with its associated active community, software and tool support) the Flux Balance Constraints package aims to extend SBML Level 3 core by adding the elements necessary to encode current and future constraint based models

SBML Level 3 Package: Flux Balance Constraints version 2

Constraint-based modeling is a well established modeling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size and complexity such steady-state flux models are, typically, analyzed using constraint-based optimization techniques, for example, flux balance analysis (FBA). The Flux balance constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modeling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. Version two expands on the original release by adding official support for encoding gene-protein associations and their associated elements. In addition to providing the elements necessary to unambiguously encode existing constraint-based models, the FBC Package provides an open platform facilitating the continued, cross-community development of an interoperable, constraint-based model encoding format

Progress report: SBML Level 3 package FBA

The SBML Level 3 "FBA" package is a proposal for an extension to the current Level 3 Core specification that allows for the description and annotation of constraint based models.

This allows one to e.g. store information related to flux balance analysis in SBML Level 3 models

FAME, the Flux Analysis and Modeling Environment

<p>Abstract</p> <p>Background</p> <p>The creation and modification of genome-scale metabolic models is a task that requires specialized software tools. While these are available, subsequently running or visualizing a model often relies on disjoint code, which adds additional actions to the analysis routine and, in our experience, renders these applications suboptimal for routine use by (systems) biologists.</p> <p>Results</p> <p>The Flux Analysis and Modeling Environment (FAME) is the first web-based modeling tool that combines the tasks of creating, editing, running, and analyzing/visualizing stoichiometric models into a single program. Analysis results can be automatically superimposed on familiar KEGG-like maps. FAME is written in PHP and uses the Python-based PySCeS-CBM for its linear solving capabilities. It comes with a comprehensive manual and a quick-start tutorial, and can be accessed online at <url>http://f-a-m-e.org/</url>.</p> <p>Conclusions</p> <p>With FAME, we present the community with an open source, user-friendly, web-based "one stop shop" for stoichiometric modeling. We expect the application will be of substantial use to investigators and educators alike.</p

A systematic assessment of current genome-scale metabolic reconstruction tools

Background: Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. Results: In this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool’s output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features. Conclusions: Model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software

Fast Flux Module Detection Using Matroid Theory

International audienceFlux balance analysis (FBA) is one of the most often applied methods on genome-scale metabolic networks. Although FBA uniquely determines the optimal yield, the pathway that achieves this is usually not unique. The analysis of the optimal-yield flux space has been an open challenge. Flux variability analysis is only capturing some properties of the flux space, while elementary mode analysis is intractable due to the enormous number of elementary modes. However, it has been found by Kelk et al. (2012) that the space of optimal-yield fluxes decomposes into flux modules. These decompositions allow a much easier but still comprehensive analysis of the optimal-yield flux space. Using the mathematical definition of module introduced by Müller and Bockmayr (2013b), we discovered useful connections to matroid theory, through which efficient algorithms enable us to compute the decomposition into modules in a few seconds for genome-scale networks. Using that every module can be represented by one reaction that represents its function, in this article, we also present a method that uses this decomposition to visualize the interplay of modules. We expect the new method to replace flux variability analysis in the pipelines for metabolic networks

Extracellular superoxide dismutase (SOD3) regulates oxidative stress at the vitreoretinal interface

Oxidative stress is a pathogenic feature in vitreoretinal disease. However, the ability of the inner retina to manage metabolic waste and oxidative stress is unknown. Proteomic analysis of antioxidants in the human vitreous, the extracellular matrix opposing the inner retina, identified superoxide dismutase-3 (SOD3) that localized to a unique matrix structure in the vitreous base and cortex. To determine the role of SOD3, Sod3-/- mice underwent histological and clinical phenotyping. Although the eyes were structurally normal, at the vitreoretinal interface Sod3-/- mice demonstrated higher levels of 3-nitrotyrosine, a key marker of oxidative stress. Pattern electroretinography also showed physiological signaling abnormalities within the inner retina. Vitreous biopsies and epiretinal membranes collected from patients with diabetic vitreoretinopathy (DVR) and a mouse model of DVR showed significantly higher levels of nitrates and/or 3-nitrotyrosine oxidative stress biomarkers suggestive of SOD3 dysfunction. This study analyzes the molecular pathways that regulate oxidative stress in human vitreous substructures. The absence or dysregulation of the SOD3 antioxidant at the vitreous base and cortex results in increased oxidative stress and tissue damage to the inner retina, which may underlie DVR pathogenesis and other vitreoretinal diseases

Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/