jAGN is a cross-platform Java based tool that allows the user to create, load, visualize and simulate the dynamics of an artificial gene network (AGN).
Specifically, jAGN provides an Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data, which can be used by computational methods to recover the network topology, and then, analyse the results based on complex networks measurements/topology.
jAGN is a cross-platform utility capable of running on any operating system that comes with Java support (e.g. Mac OS X, Windows, Linux).
Here are some key features of "jAGN":
· (Integer) Number of Genes: This parameter defines the number of genes (vertices or nodes) of a network (graph).
· (Combo Box) Topology: This parameter defines the theoretical model of complex network (topology) for generate the AGN structure, i.e., the connections among genes.
· (Integer) Average Degree: This parameter defines the average degree of edges (connections) per gene (vertex or node).
· (Integer) Signal Size: This parameter defines the number of instants of time, i.e., the signal size.
· (Integer) Initializations: This parameter defines the number of different initializations with random initial values, which were concatenated in one single temporal signal.
· (Check Box) Separate the signal?: If the number of initializations is greater than zero, this option includes a column separating different initializations.
· (Combo Box) Transition functions: This parameter defines the transition functions, which simulate the expression dynamics. For now available only Boolean functions.
· (Check Box) All Boolean Functions?: This option includes all possible Boolean functions as transition functions, i.e., if a target has two predictors, there are 2^2^2 = 16 possible Boolean functions that can be used to represent the functional dependency between the target and its predictors. On the other hand, there are Boolean functions that do not depend on one or more predictors, such as contradiction (always false) and tautology (always true), to name but a few. If this check box is not marked, only Boolean functions that depend on all predictors are considered as transition function.
· (Integer) Number of Boolean Functions per Gene: There are other two parameters that guide the stochasticity of the model: the number of Boolean functions per gene and its probabilities of being used to describe the dynamics of each gene. This is done in order to consider an external stimuli on network dynamics, which would change the behavior (transition function) of the organism.
· (Float) Concentrated probability: This parameter defines the probability of occurrence of the main transition function of each gene target. The complement of the probability (1-concentrated probability) is divided equally among the other transition functions, i.e., considering the chosen number of transition functions per gene.
· (Text Field) Network File: A previously generated network can be loaded into the application. This field store the path of the network file (.agn).
· (Button) File: Shows an open dialog to select input data file (.agn). The path of the selected file is stored automatically at Network File text field.
· (Button) Load Network: Loads the network specified in the text box on the application.
· (Button) Generate AGN: Creates a new artificial gene network and its dynamics according to the specified parameters. The generated dynamics is displayed in the table Generated Data.
· (Button) Show Network: Displays a new window with a visual representation of the the network as a graph, in which genes are pesented as nodes and its relationships as edges.
· (Button) Adjacency Matrix: Displays the adjacency matrix representation of the network, in which each edge (connection) from gi to gj, i.e., gi --> gj implies M(i,j)=1, with M(i,j)=0 otherwise.
· (Button) Histograms: Displays 3 new windows with Cumulative Distribution, In-degree and Out-degree Histograms of the network genes (vertices or nodes).
· (Button) Save AGN: Option to save the generated network into a binary file (.agn).
· (Table) Generated Data: Shows the dynamics of the AGN, which is then obtained by applying transition functions on a randonly chosen initial state. In other words, the dynamics of the AGN is modeled by applying the Boolean transfer functions while considering a given network initial state at time 0 and the number of instants of time T (Signal Size).
· (Button) Save Generated Data: Click to save the generated data into a text file.