PathwayOracle Toolkit

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

Abstract PathwayOracle is an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network.

The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a ‘signaling network’ to various experimental conditions and stimuli using only the connectivity of the signaling network.

Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions.

In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis - loading and superimposing experimental data, such as microarray intensities, on the network model.

PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results.

By using the signaling network connectivity; analyses and predictions can be performed quickly using relatively easily constructed signaling network models.

This application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features.

PathwayOracle is an integrated environment for connectivity-based structural and dynamic analysis of signaling networks, supporting:

Visualization of signaling network connectivity;

Two (2) versions of the simulation method described in the paper (The Signaling Petri Net-based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks PLoS Computational Biology 2008, 4(2):e1000005 - see below...) where -

1) The first allows prediction of signal flow through a given network for a specific experimental condition, and

2) The second predicts the difference in signal flow through a given network induced by two (2) different experimental conditions:

The Signaling Petri Net Simulator --

Petri nets provide a graphical and executable model of processes in which information or material flows among a series of places or entities. A Petri net consists of places, transitions, and tokens. Quantities of tokens are assigned to individual places.

This assignment is called a marking. The network flow is modeled by the reassignment of tokens to individual places in the Petri net in response to transition firings.

A signaling Petri net is an extension of the Petri net formalism to model a signaling network. Places are signaling proteins and transitions implement directed protein interactions; each transition models the effect of a source protein on a target protein.

The marking of (number of tokens in) protein p at time t is interpreted as the activity-level of that protein - the number of activated molecules of that type.

The signaling Petri net simulator models signal flow as the pattern of token accumulation and dissipation within proteins over time in the Petri net.

Through transition firings, the source can influence the marking of (the number of tokens assigned to) the target, modeling the way that signals propagate through protein interactions in cellular signaling networks.

PathwayOracle Implementation --

The user experience is oriented around the visualization of and interaction with three (3) main types of data: the signaling network, markings, and paths. At any given time, one signaling network is open, which is the basis for all analyses.

Any simulation or concentration data is loaded and inspected as markings. Currently all static analyses revolve around paths, which are the third data type.

PathwayOracle Results --

PathwayOracle provides a variety of tools for analyzing the structural and dynamic properties of a signaling network based on its connectivity.

While its main differentiating feature is the ability to predict signal flow through a network using only the connectivity of the signaling network, PathwayOracle also provides the ability to visualize the network, analyze its connectivity, and inspect concentration-based experimental data.

Network Visualization -

An interactive graphical representation of the signaling network connectivity is at the center of the PathwayOracle interface. The main window provides a visualization of the signaling network connectivity.

This visualization interface allows the user to edit the layout of the network by clicking on and dragging nodes and by shift-clicking on edges to create, remove, or move waypoints. Waypoints are points that lie on an edge.

The network visualization also provides a view onto which path and experimental data analysis may be mapped. Selected paths may be highlighted in this view and markings from experiments can set the colorings of individual nodes.

Network Signal Flow Simulation -

The main feature differentiating PathwayOracle from other tools such as CellDesigner and COPASI is its ability to simulate signal flow using an unparameterized signaling network model.

Simulations can be performed in two (2) different ways:

In the first (Single Simulation), the simulator predicts the signal flow through the network for a specific experimental condition.

In the second (Differential Simulation), the simulator predicts the difference in signal flow due to two different experimental conditions on the same network.

Signaling Path Analysis -

The use of the simulators and plotting tools allows the user to observe trends in the activity-level of individual signaling nodes over time.

Since the activity-level of a node is determined by the activity-level of other nodes in the network, the activity-level time series of a node may be explained by changes in the activity-level history of nodes upstream of it.

In order to investigate such indirect interactions, it is useful to enumerate all the paths leading from a specific protein to the protein of interest. PathwayOracle provides this capability.

Additionally, it provides various statistics on the set of paths linking two signaling nodes as well as a classification of the effect of each path as either coherent or incoherent.

Experimental Data Analysis -

A model of the connectivity of a signaling network makes it possible to identify components of the model that are inconsistent with experimental data or visa versa.

PathwayOracle enables this kind of analysis by allowing users to load experimental concentration data and visualize it both as a heatmap or superimposed on the network view.

In PathwayOracle, experimental concentration data is loaded as a marking group in which a single marking corresponds to a condition for which concentrations were sampled.

PathwayOracle Future Directions --

The manufacturer considers future directions for PathwayOracle to fall into several categories: network construction, network augmentation, experimental and computational analysis integration, and architecture. A longer term direction for PathwayOracle is the integration of transcriptional and metabolic network analysis.

Currently, PathwayOracle employs a modular architecture that facilitates easy integration of new functionality.

However, in future releases the manufacturer plans to expose a plug-in interface which will make it easier for developers and researchers to develop and deploy tools within PathwayOracle.

System Requirements

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Manufacturer

Manufacturer Web Site PathwayOracle Toolkit

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G6G Abstract Number 20616

G6G Manufacturer Number 104217