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= Variation networks =
 
= Variation networks =
  
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The concept of variation networks derived from the processes of network comparison and simulation filtering.
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The concept of variation networks was development by combining network comparison with simulation filtering, essentially these are networks which are obtained by comparing two or more networks derived from the same base using different filters and creating a third network with only the parts which differed from one network to another (the variations). This variation network can then be used as a tool to compare the different simulated situations which can be particularly useful for the analysis of a mutant organism if it is compared with a wildtype.
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A variation network is obtained by comparing a pair of networks obtained through simulation filtering, using the same base network and simulation results obtained under different conditions. The result is the creation of a third network containing the elements that differed between the compared networks (i.e. the variations).
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The idea behind the use of variation networks is to identify the components of a given metabolic system that change when the conditions inherent to the simulations change. Assuming the dimension of these variations to be significantly smaller than the networks themselves, variation networks should be easier to analyze than a full network.
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Initially the concept of variation networks worked only with a pair of networks but latter it was expanded to work with solution sets, in this case one network is taken as reference (usually the wildtype) and it is compared with each network in the solution se, in the end the variation network obtained contains only the more commune variations, based in an user defined threshold.
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The most important decision in this process is to select how the variations between the networks should be identified, i.e. what criteria should be used to decide on the components to be selected. Two methods are included in TNA4OptFlux:
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* 1 - Exclusivity: this is the simplest method but it has already proven useful in analyzing phenotype simulation results. The criterion used is based on the identification of the exclusive reaction vertices in each network, i.e. those existing in one of the networks but not the other. After the identification of these reactions, the respective vertices are added to the variation network, together with the vertices corresponding to metabolites that they consume or produce, as well as the edges connecting them.
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TNA4OpfFlux uses two variation metrics: exclusivity (a vertex is exclusive if it belongs only to one network) and flux variation (if a reaction flux changed over a user defined threshol).
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* 2 - Flux variation: This second method was developed after it was verified that methods based purely on network topology can be, in some cases, insufficient to capture more subtle metabolic flux variations. The flux variation is based in the comparison of flux values of the reactions present in both networks: if the absolute value of the difference in fluxes exceeds a user defined threshold (which can be an absolute flux value or a percentage) then the vertex corresponding to that reaction, as well as the ones corresponding to the metabolites which participate in it and the edges which connect them, are added to the variation network.
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These two methods can be used independently or combined. After a variation network is created, it can then be analyzed as any other network using any of the tools available in the plug-in.
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To create an variation network from a pair o networks with associated flux data:
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Besides the comparison of pairs of networks, the concept of variation networks was expanded to work with simulation sets, i.e. sets of phenotype simulations. For creating a variation network from a simulation set, one network not belonging to the set (e.g. obtained from an wild type simulation) is considered the reference network. This network is compared to all networks of the set using the same methods as before. In this case, vertices are only included in the final variation network if they have shown a significant variation in at least K comparisons (where K is a user defined parameter).
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* 1 - Go to Analysis -> TNA4 -> Varition networks -> create variation network from network pair.
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To obtain a variation network from a pair of network though simulation filtering go to Plugins →TNA4 → Variation Networks → Create varation networks from network pair. Then select the pair of networks to be compared. Finally select the variation criteria, including the flux variation threshold (which can be a relative or absolute value) if this criteria is used.
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* 2 - Select the two networks, booth must belong to the same project and have associated flux data.
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To obtain a variation network from multiple networks first make all necessary simulation and combine them in a set by using Simulation → Create Simulation Set. Then go to Plugins →TNA4 → Variation Networks → Create varation networks from solution set. Select a network obtained though simulation filtering to serve as the reference network and the solution set. Next select the variation criteria and define the K value for each by felling the text box in front of the name of the selected criteria, please note that K must be a value between 0 and 1. Please note that when using a solution set it is only necessary to create the reference network.
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* 3 - Select the variation metrics to use.
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** 3.1 - If flux variation is used a threshold, either absolute value or an percentage (0.0 to 1.0) must be selected.
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<br>
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To create an variation network from a solution set:
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* 1 - Go to Analysis -> TNA4 -> Varition networks -> create variation network from solution set.
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* 2 - Select a project and a wild type simulation result.
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* 3 - Select the simulation results to be compared to the wild type.
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*4 - Select the variation metrics to use and the minimal threshold.
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**4.1 - If flux variation is used a threshold, either absolute value or an percentage (0.0 to 1.0) must be selected
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[[Image:TNA4OptFlux3.png|frame|center|914x319px|Sample variation network]]
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Please note that when creating a variation network from a solution set it is not necessary to first create a network for each solution.

Revision as of 17:08, 15 January 2013

How to create a network

TNA4OptFlux networks are created by extracting data from the mathematical models used by OptFlux to run simulations, in OptFlux each mathematical model is associated with a project consequently before using TNA4OptFlux at least one project has to exist, created networks are associated with the project from whose modal they derived.


1) Reaction-Metabolite network; 2) Reaction only networks; 3) Metabolite only network

To create a network:

  • 1 - Go to Analysis -> TNA4 -> New network.
  • 2 - Select the project, the type of the network, name your new network and decide if it should include drain and/or external reactions. There are three possible types of networks:
    • 2.1 - Reaction-Metabolite networks: these are bipartite graphs where the vertices represent the reactions and the metabolites.
    • 2.2 - Metabolite only networks: only contain metabolite vertices and the edges connect the compounds which are converted from one to another by the reactions.
    • 2.3 - Reaction only networks only contain reaction vertices and the edges connect reactions to the ones which consume their products (Warning: the creating of this networks usually takes significantly more time than the other two types).
  • 3 - If filters are used then rest of the process is identical to apply a filter to an already existing network.

Using ranking algorithms

Ranking algorithm was classification given to any algorithm which analysis a network and give to each vertex a ranking value, following this definition TNA4OptFlux implements four ranking algorithm:

  • 1 - Betweenness Centrality
  • 2 - Closeness Centrality
  • 3 - HITS (Hubs-and-authorities)
  • 4 - Clustering coefficient

To use any ranking algorithm just go to Analysis -> TNA4 -> Ranking algorithms and select the desired one from the menu.

Exporting networks

TNA4OptFlux networks can be exported as files, currently three file formats are suported:

  • 1 - Pajek format - this format is used by the Pajek network analysis application, networks exported in this format are converted to two files: a network file and a partition file.
  • 2 - MBNF format - this is the default format of InBiNA, the network analysis aplication that TNA4OptFlux is based on.
  • 3 - XGMML format - this is an XML format which cytoscape supports, it is especially useful when dealing with variation networks because the flux values are also exported.

To export a network go to Analysis -> TNA4 -> Export and select the desired format.

Shortest path metrics

Besides simply identifying the shortest paths between two vertices, TNA4OptFlux supports several other analysis functionalities base in the shortest path.

To use TNA4OptFlux's shortest path functionalities :

  • 1 - Select to Analysis -> TNA4 -> Get Shortest Path Metrics.
  • 2 - Select the network.
  • 3 - There is now a "shortest path metrics" object associated with the network which contains the shortest path information divided by four panels:
    • 3.1 - The first panels contains the generic networks shortest path data: the diameter, average shortest path, larger shortest path in the network and number of vertices with inward and outward edges.
    • 3.2 - The second panel allows user to calculate the distance between two select vertices alternatively the user can visualize one shortest path or all possible shortest path.
    • 3.3 - The third panel can be used to obtain generic information about individual vertices.
    • 3.4 - Finally the fourth panel contains a table with an histogram of the shortest path.

Independent modules (or subgraphs)

Some networks contain subgraphs, or independent modules, these are parts of the network which are isolated from the rest of the network, independent modules shouldn't be present in metabolic networks obtained directly from a model, but when dealing with filtered networks they can occur

To identify the independent modules present in a network using TNA4OptFlux select Analysis -> TNA4 -> Indentify independent modules, the independent modules can then be converted into networks if more in-depth analysis of them is required.

Filters

Filters are used to create new networks by removing elements from existing ones which can be useful when dealing with extensive networks or when only part of one network is of interest.

  • 1 - To use a filter go to Analysis -> TNA4 -> Filter network.
  • 2 - Select the network, the project it is associated with and the name of the filtered network to be created.
  • 3 - Chose if the filtered network should contain drain reactions and/or external metabolites.
  • 4 - If there are simulations associated with the project decide if simulation filtering (removal of the parts of the network which were not active or preformed under a selected threshold in a simulated scenario) should be used.
  • 5 - Chose the type of degree based filtering to be executed (if any), currently the types supported are:
    • 5.1 - Removal of metabolites with a inward degree above a set threshold.
    • 5.2 - Removal of metabolites with a outward degree above a set threshold.
    • 5.3 - Removal of metabolites with a degree above a set threshold.
    • 5.4 - Removal of the k metabolites (value chosen by user) with higher inward degree.
    • 5.5 - Removal of the k metabolites (value chosen by user) with higher outward degree.
    • 5.6 - Removal of the k metabolites (value chosen by user) with higher degree.
  • 6 - If any ranking algorithm was used decide if it should be used as an filtering metric.
  • 7 - Select individual vertices to remove.
  • 8 - Select individual edges to remove.

It should be noted that regardless of the filtering method choose vertices with an degree of zero will be removed from the final network.

Degree distribution

To calculate the degree distribution of a network select Analysis -> TNA4 -> Degree distribution.

Network comparison

Using TNA4OptFlux it is possible to compare a pair of networks to do so select Analysis -> TNA4 -> Compare networks and select the network pair. Comparisons are made using the network structure and any analyses metrics which were applied to booth of the networks previously to the comparison.

Most of the comparison functionalities are straightforward however one deserves further explanation: the concept of decision points, these are metabolites which are present in booth networks but are consumed by different sets of reactions each. Decision points received their name because they can be considered parts of the network were there was a "decision" about the flux matter in the metabolism.

Locating active vertices

The location of active vertices is an functionality of TNA4OptFlux which can be used to give an idea of which reactions should active if certain metabolites are present in the environment. It is a recursive algorithm based in the petri net like structure that a Reaction-Metabolite networks posses. To use this functionality:

  • 1 - Go to Analysis -> TNA4 -> Locate Active Vertices.
  • 2 - Select from the list of metabolites the ones which are supposed to be present in the environment.


Example of the location of active vertices algorithm


Please not that this functionality only works with Reaction-Metabolite networks.

Calculating flux variation

When simulation filtering is used the resulting subnetwork contains the values of the reactions fluxes which were used in the filtering operation. TNA4OptFlux can be used to compare the flux values associated with an networks obtained via simulation filtering, this functionality doesn't pertain directly to network analysis but it can help users compeering different simulation which complements the analysis of variation networks nicely.

To use compare the flux values:

  • 1 - Go to Analysis -> TNA4 -> Calculate flux variation.
  • 2 - Chose the project and network.
  • 3 - Select an reference network, alternatively the fluxes can be compared with the results of an wildtype simulation obtained via pFBA.


Please note that only networks that were created though simulation filtering have associated flux values, consequently they are the only ones that can be used with this analysis tool.

Variation networks

The concept of variation networks was development by combining network comparison with simulation filtering, essentially these are networks which are obtained by comparing two or more networks derived from the same base using different filters and creating a third network with only the parts which differed from one network to another (the variations). This variation network can then be used as a tool to compare the different simulated situations which can be particularly useful for the analysis of a mutant organism if it is compared with a wildtype.
Initially the concept of variation networks worked only with a pair of networks but latter it was expanded to work with solution sets, in this case one network is taken as reference (usually the wildtype) and it is compared with each network in the solution se, in the end the variation network obtained contains only the more commune variations, based in an user defined threshold.
TNA4OpfFlux uses two variation metrics: exclusivity (a vertex is exclusive if it belongs only to one network) and flux variation (if a reaction flux changed over a user defined threshol).
To create an variation network from a pair o networks with associated flux data:

  • 1 - Go to Analysis -> TNA4 -> Varition networks -> create variation network from network pair.
  • 2 - Select the two networks, booth must belong to the same project and have associated flux data.
  • 3 - Select the variation metrics to use.
    • 3.1 - If flux variation is used a threshold, either absolute value or an percentage (0.0 to 1.0) must be selected.


To create an variation network from a solution set:

  • 1 - Go to Analysis -> TNA4 -> Varition networks -> create variation network from solution set.
  • 2 - Select a project and a wild type simulation result.
  • 3 - Select the simulation results to be compared to the wild type.
  • 4 - Select the variation metrics to use and the minimal threshold.
    • 4.1 - If flux variation is used a threshold, either absolute value or an percentage (0.0 to 1.0) must be selected


Sample variation network


Please note that when creating a variation network from a solution set it is not necessary to first create a network for each solution.

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