C13 is a tool for quantification of in vivo metabolic fluxes, from carbon labelling experiments. The underlying approach is metabolic flux analysis based on stationary carbon isotope labelling experiments, using fractional enrichment data. The mathematical framework adopted herein is the one developed by Wiechert and de Graaf (1997).
BioOpt is a software application running on Windows command prompt. The program focuses on the flux balance analysis, using linear programming as the mathematical support. Given a biological system model, which includes a set of metabolic reactions, the program is able to calculate all internal mass balance fluxes, reduced costs and shadow prices depending on the constraints and objective defined by the user. Running BioOpt with different parameters allows the user to obtain several kinds of outputs that can help in the analysis of the system.
Consensus gene set analysis
Piano combine the results from different runs of GSA into a consensus result in a heatmap. The consensus score is the mean rank given each gene set by the different GSA runs. This means that a low
score is a gene set that is ranked high by most of the GSA methods.
With a GEM as input, this function displays all reactions and fluxes on the screen. This tool checks which reactions can carry a flux (positive or negative) using linear programming as mathematical support.
Gene set analysis
The Gene set analysis function collects a number of GSA methods into the same platform, making it easier to test different methods using the same settings, format and input.
Online tools powered by RAVEN and PIANO in the toolbox.
Microarray differential expression analysis
Differential expression analysis are carried out with simple and most common approach. The user has to define atleast 2 conditions that are to be compared, and the genes that will differ between these conditions will be detected.
Microarray quality check
This tool uses runQC function that collects a selection
of quality check methods and lets you run them on your loaded and preprocessed data.
The methods that are possible to run depends on if you started from raw data or not
GEM Overview provides the summary information of the model including the number of genes, reactions and metabolites for each compartment and Exchange reactions.
It also tells us about the metabolites that can be produced or consumed by the model.
In this function, two models showing different conditions are compared returning a number of random solutions for the model. The solutions are generated by maximizing (with random weights) for a random set of three reactions. For reversible reactions it randomly chooses between maximizing and minimizing.
This tool identifies those metabolites around which transcriptional changes occur.
Reporter features is a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data. The algorithm is an extension of the concept of Reporter Metabolites that allows the identification of key biological features around which transcriptional changes are significant. The goal of the tool is to integrate biological networks composed by features like protein protein interactions, Gene Ontology association, transcription factor interaction and complexes formation.
The integration of transcriptome data and large scale biological networks has made possible the study of regulatory circuits, protein functions and evolution. To find active Subnetworks within the large scale biological network it has been necessary to implement simulated annealing programming algorithms. These Subnetworks are connected regions of the large scale biological network that show significant changes in expression toolBoxOver particular subset of conditions. Following this theory, Subnetwork Analysis tool uses the enzyme interaction network constructed from the genome-scale models and performs a screening to identify significantly correlated metabolic Subnetworks after direct or indirect perturbations of the metabolism.
SBML 2 BioOpt
This tool converts models in systems biology markup language (SBML) format to BioOpt format. In the online versions of the tools you generaly can chose to use SBML directly and let the online tool make the conversion.
SBML to BioOpt Format
Required input in BioOpt tool is a genome-scale model in BioOpt format, so a converter from SBML to BioOpt is provided in the old version of BioMet toolbox and hence it is possible to use custom models available in SBML format in addition to those provided in the library of models
Given a GEM as an input, this tool prints warnings about the unbalanced reactions (Elemental balance), those reactions that are no longer in use (Dead end Reactions), those metabolites that are no longer being used (Dead end metabolites), and all the metabolic subgraphs from the model.