Figure 4: Signaling network topology for RANTES based on the top ten signaling metrics. The kinases JNK and ERK1/2 were found to play an important role in regulating RANTES from the PLS analysis. Legends shown in the top row were identified directly from the data (i.e. not a model output) as the top activators of either JNK or ERK1/2. Also see Table 3.

Mentions: Introduced earlier, Figure 2 shows the squared weighted VIP profile for RANTES. This information is further summarized in a basic network diagram that highlights the most important input and signaling variables as determined through the model (Figure 4). Here the top 10% time-related, activation state properties of JNK and ERK1/2 are shown in the middle row. Also shown in the network for both JNK and ERK1/2 are the top 5 most significant input stimuli (i.e. those stimuli that caused the greatest increase in JNK or ERK1/2 phosphorylation state).

Wu Y, Johnson GL, Gomez SM - J Mol Signal (2008)

Bottom Line: We paid particular attention to the effect of metrics extracted from the experimentally derived signaling time courses so as to determine whether the inclusion of such temporal information improved model predictions.Furthermore, for this data set, the use of time metrics was found to be of mixed value, with increased and/or more appropriate sampling likely being required to improve predictive performance.The use of multivariate approaches and model averaging provides a valuable predictive framework for quantitative studies of these complex biological processes.Results of this work also point to several issues for consideration in the design of similar large-scale interrogations.

Affiliation: Joint Department of Biomedical Engineering, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA. smgomez@unc.edu.

Abstract: Understanding the relative importance of signaling pathway components which regulate a specific cellular response is a major focus of current efforts in biology. This interest, along with the inherit complexity of these systems, is driving the development of approaches capable of providing both quantitative predictions as well as guiding the design of future experiments. Of particular interest is the establishment of methods for the analysis of cellular-level input-output signaling relationships that have been characterized over time.Work by the Alliance for Cellular Signaling (AfCS) has provided an extensive profile of ligand-induced changes in protein phosphorylation state and cytokine output response in macrophage-like RAW 264.7 cells. Using model averaging with partial least squares (PLS) or principal components regression (PCR), we compared multivariate models quantitatively predicting cytokine release and identifying key regulatory components of the underlying signaling pathways. We paid particular attention to the effect of metrics extracted from the experimentally derived signaling time courses so as to determine whether the inclusion of such temporal information improved model predictions. Results indicate that we were able to determine the key biological predictors responsible for generating a specific cytokine response, with model R2 values ranging from 0.48 to 0.93. Furthermore, for this data set, the use of time metrics was found to be of mixed value, with increased and/or more appropriate sampling likely being required to improve predictive performance.The use of multivariate approaches and model averaging provides a valuable predictive framework for quantitative studies of these complex biological processes. Results of this work also point to several issues for consideration in the design of similar large-scale interrogations.