BioMet Toolbox

Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools and Cross-Omics>Agent-Based Modeling/Simulation/Tools

Abstract BioMet Toolbox is a web-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models.

The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated.

These tools can be used for high-throughput in silico screening and allows for fully standardized simulations. Model files for various model organisms (fungi and bacteria) are included.

Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks.

The BioMet Toolbox also includes Genome-Scale Metabolic Models (GSMMs) of various cell factories used both in industrial biotechnology and in fundamental research.

The major advantage of BioMet Toolbox is that it combines flux balance analysis, transcriptome analysis and omics data integration in a single package with a user-friendly interface which makes it available for a broad audience.

BioMet Toolbox Features/capabilities --

The BioMet Toolbox consists of:

1) A suite of applications (‘Tools’) developed for studying metabolism and high-throughput analysis; and

2) A collection of GSMMs (‘Models’) of different organisms.

Tools -

The core of the Tools section consists of a client applet that acts as a graphical interface to the server with the following analyses methods:

1) Calculation of all internal mass balance fluxes, reduced costs and shadow prices for the assessment of in silico metabolic model predictive capabilities (BioOpt - see below...);

2) Identification of key biological features (metabolites, transcription factors, protein-protein interactions and GO association) around which transcriptional changes are significant (Reporter Features - see below...); and

3) Identification of significantly correlated metabolic sub-networks after direct or indirect perturbations of the metabolism (Reporter Subnetwork - see below...).

Each analysis has a choice of sub-options that become available on its selection together with extensive examples and a Help menu.

BioOpt - Flux distribution calculations using flux balance analysis -

BioOpt focuses on flux balance analysis (FBA), using linear programming as the mathematical support. Flux distribution calculations using FBA is a widely used method for analysis of the capabilities of a metabolic network.

Given a set of constraints, such as maximal uptake rates of nutrients, BioOpt returns the set of metabolic fluxes that maximizes a specified objective function (usually the growth rate of the organism).

FBA of genome-scale networks provide an excellent platform for evaluation of gene essentiality as well as the more general study of ‘metabolic perturbations’ following gene knockouts.

This has been used to identify drug targets and to suggest possible metabolic engineering strategies to optimize by-product formation in microbial fermentations.

BioOpt implements several analyses to deal with this type of problem, including an exhaustive combinatorial search for combinations of gene deletions and a mixed integer linear programming application to identify the best set of gene deletions given a target objective value.

Other analyses include over-expression of fluxes and basic sensitivity analysis.

Reporter Features - Identification of transcriptional regulatory circuits in metabolic networks -

Reporter Features is a hypothesis-driven algorithm that integrates transcriptome/proteome/metabolome data with the topology of bimolecular networks. The Reporter Features algorithm exploits the connectivity structure of bio-molecular interaction networks for data integration.

For example, the metabolic network can be treated as a bipartite undirected graph, where the nodes are the metabolites and the enzymes composing each reaction, while the edges represent the association between the metabolites and enzymes due to the corresponding reactions.

All enzyme nodes are scored based on the P-value for the significance of change in the expression level of the corresponding gene (across different conditions/mutants). Each metabolite in the graph is then statistically assessed for collective transcriptional response in the neighboring enzyme nodes.

Metabolites with significant scores represent metabolic hot spots with a significant degree of transcriptional regulation around them.

The algorithm can also be applied to different biological networks to identify corresponding Reporter Features [such as transcription factors, protein-protein interactions, Gene Ontology (GO) association, protein complexes and ad hoc interactions of interest] around which transcriptional changes are collectively significant.

For example, if the algorithm is applied to the regulatory network that represents interactions between transcription factors (TFs) and the regulated genes, the result will mark the reporter transcription factors - indicating significant change in the corresponding TF activities.

Reporter Subnetwork - Identification of significantly responsive/correlated metabolic subnetworks -

The aim of the Reporter Subnetwork tool is to identify significantly responsive/correlated metabolic subnetworks after direct or indirect perturbations of the metabolism.

Interaction networks are constructed based on the metabolic genes. Specifically, metabolic enzymes are represented as an undirected graph by using the topological information from the Genome-Scale Metabolic Model (GSMM).

In such a undirected graph, two metabolic enzymes are connected if they share any common metabolite in the corresponding reactions.

Note: The tools described above represent valuable resources for exploring the capabilities of metabolic networks. The user can perform analysis on the models from the manufacturer's database or upload their own custom GSMM(s).

System Requirements

Contact manufacturer.


Manufacturer Web Site BioMet Toolbox

Price Contact manufacturer.

G6G Abstract Number 20631

G6G Manufacturer Number 104231