Data-driven modeling of cellular stimulation, signaling and output response in RAW 264.7 cells

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.