Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods.Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity.We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

To identify regulators of intracellular signaling we targeted 541 kinases and kinase-related molecules with siRNAs and determined their effects on signaling with a functional proteomics reverse phase protein array (RPPA) platform assessing 42 phospho and total proteins. The kinome wide screen demonstrated a strong inverse correlation between phosphorylation of AKT and MAPK with 115 genes that when targeted by siRNAs demonstrated opposite effects on MAPK and AKT phosphorylation. Network based analysis identified the MAPK subnetwork of genes along with p70S6K and FRAP1 as the most prominent targets that increased phosphorylation of AKT, a key regulator of cell survival. The regulatory loops induced by the MAPK pathway are dependent on TSC2 but demonstrate a lesser dependence on p70S6K than the previously identified FRAP1 feedback loop. The siRNA screen also revealed novel bi-directionality in the AKT and GSK3 interaction, whereby genetic ablation of GSK3 significantly blocks AKT phosphorylation, an unexpected observation as GSK3 has only been predicted to be downstream of AKT. This method uncovered novel modulators of AKT phosphorylation and facilitated the mapping of regulatory loops.

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Distribution coefficients (D) were measured for flavone, monohydroxyflavones, and monomethoxyflavones equilibrated between 1-octanol and aqueous buffer (50 mM MOPS, pH=7.4). The values of LogD were used to determine substitution constants referred to as π values. Hydroxyl groups at the 3 or 5 position of flavone had positive π values (increased hydrophobicity) while hydroxyl groups at other positions had negative π values (increased hydrophilicity). For each monohydroxyflavone, chromatographic capacity factors (k') were determined for both reverse phase (C-18) and immobilized artificial membrane (IAM) columns. For the IAM column, relatively large k' values were observed for both 3-hydroxyflavone and 5-hydroxyflavone indicating that hydroxyl groups at positions 3 and 5 of flavones promote high affinities for phospholipid structures. These results should aid in refinement of quantitative structure activity relationships (QSAR's) that are useful for drug development based on flavonoids as lead compounds.

Mapping intra-cellular signaling networks is a critical step in developing an understanding of and treatments for many devastating diseases. The predominant ways of discovering pathways in these networks are knockout and pharmacological inhibition experiments. However, experimental evidence for new pathways can be difficult to explain within existing maps of signaling networks.In this paper, we present a novel computational method that integrates pharmacological intervention experiments with protein interaction data in order to predict new signaling pathways that explain unexpected experimental results. Biologists can use these hypotheses to design experiments to further elucidate underlying signaling mechanisms or to directly augment an existing signaling network model.When applied to experimental results from human breast cancer cells targeting the epidermal growth factor receptor (EGFR) network, our method proposes several new, biologically-viable pathways that explain the evidence for a new signaling pathway. These results demonstrate that the method has potential for aiding biologists in generating hypothetical pathways to explain experimental findings.Our method is implemented as part of the PathwayOracle toolkit and is available from the authors upon request.