Background In systems biology the experimentalist is normally presented with a selection of software for analyzing dynamic properties of signaling networks. which differentiates em PathwayOracle /em from other tools is a method that can predict the response of a signaling network to numerous experimental conditions and stimuli using only the connection of the signaling network. Therefore signaling models are relatively easy to build. The method allows for tracking signal circulation in a network and assessment of transmission flows under different experimental circumstances. Furthermore, em PathwayOracle /em includes equipment for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental evaluation C loading and superimposing experimental data, such as for example microarray intensities, on GW 4869 distributor the network model. Bottom line em PathwayOracle /em has an integrated environment where both structural and powerful evaluation of a signaling network could be quickly executed and visualized with experimental results. Utilizing the signaling network online connectivity, analyses and predictions can be carried out quickly using fairly quickly built signaling network versions. The application form has been established in Python and was created to be quickly extensible by groupings thinking about adding brand-new or extending existing features. em PathwayOracle /em is openly designed for download and make use of. History Reconstructing cellular signaling systems and focusing on how they function are main endeavors in cellular biology. The level GW 4869 distributor and complexity of the networks, nevertheless, render their GW 4869 distributor evaluation using experimental biology techniques alone extremely challenging. Because of this, computational strategies have been created and coupled with experimental biology techniques, producing powerful equipment for the evaluation of the networks. These equipment help biologists in interpreting existing experimental results, analyzing hypotheses, enumerating feasible biological behaviors, and, eventually, in quickly creating experiments that increase the amount of useful info gained. By assisting biologists in maximizing the amount of info acquired from their experiments through improved experimental design and more thorough analysis of results, computational tools increase the pace of scientific discovery. Biological network analysis can generally become classified as either em structural /em or em dynamic /em [1]. Structural analysis provides insights into global properties of the network, among them decomposition of the network into practical modules (e.g., [2]), enumeration of signaling paths connecting arbitrary protein pairs (e.g., [3-5]), and the identification of key pathways that determine the behavior of the network (e.g., [2,6-10]). Dynamic methods, on the other hand, simulate the actual propagation of signals through a network by predicting the changes in the concentration of signaling proteins over time. These predictions will become of varying examples of resolution and accuracy, depending mainly on the accuracy and level of fine detail of the model from which they are produced. The prevailing methods for dynamic analysis involve systems of regular differential equations (ODEs) [11,12]. These methods require kinetic parameters for the individual biochemical reactions involved in the signaling process. This requirement often poses a significant hurdle for researchers as the numerical values of such parameters are hard to obtain and may be the object of the researcher’s project in the first place. In CCL2 [13], we offered a novel signaling network simulation method which uses a non-parametric Petri net model of network to predict the signal flow under numerous experimental conditions. Our simulation method uses a novel technique to approximate the interaction speeds and predicts the qualitative behavior of the signaling network dynamics. The advantage of our method over ODEs is the wide availability of connectivity-based models of signaling networks, and the relative speed with which they can be constructed. Numerous databases exist which catalog known signaling interactions (e.g., [14-16]). Thus, the existence and type (activating or inhibition) of an interaction can often be inferred directly from literature and/or these databases. This presents a stark contrast to the kinetic parameters required by ODEs, the numerical values for many of which must be determined experimentally for each experimental condition and cell line of interest [2]. In this paper, we present the software tool em PathwayOracle /em , an integrated environment for connectivity-based structural and dynamic analysis of signaling networks, supporting ? visualization of signaling network connectivity; ? two versions of the simulation method described in [13] where – the first allows prediction of signal flow through a given network for a specific experimental condition, and – the second predicts the difference in signal flow through a given network induced by two different experimental conditions; ? enumeration of the paths connecting arbitrary pairs of nodes in the network; and ? visualization of experimental concentration data on the signaling network display. In future releases we plan on expanding capabilities in all three areas of analysis C dynamic, structural, and experimental C with a focus on providing effective ways of integrating results from each together. em PathwayOracle /em has been designed in a modular fashion in order to facilitate extension of existing capabilities and the addition of new.