(693e) Simplifying Rule-Based Reaction Networks Using Timescale Analysis: The Interleukin-12 Signaling Pathway as An Illustrative Example

Cells sense and respond to their external environment through a network of protein-protein interactions. This flow of information within cells is summarized by a cell signaling pathway. These signaling pathways ubiquitously appear in the literature as cartoons that enumerate the key proteins and their interactions that control this flow of information. Rule-based modeling techniques have been used to translate these qualitative cartoons into quantitative mathematical models. However, it is unclear how well a specific cartoon applies to a specific cell type. Timescale analysis is an engineering technique that can be used to infer what interactions within these signaling cartoons control the dynamics of cell signaling in specific systems. The objective of this work was to apply timescale analysis to a signaling network generated using a rule-based approach. As an illustrative example, we applied a timescale analysis to a model of the Interleukin-12 (IL12) signaling network that was calibrated to data obtained from primary naïve CD4+ T cells. The timescale analysis provided insight into the timescales associated with different reaction classes (i.e., reaction rules). The reaction classes were then parsed into three categories: fast (i.e., pseudo-equilibrium), medium (i.e., rate limiting), and slow (i.e., stationary). We also used an empirical Bayesian approach to establish a level of confidence associated with the timescales of the individual reaction classes. The timescale predictions were corroborated with observations reported in the literature. In summary, these three techniques were helpful to determine, at a certain level of confidence, the key protein-protein interactions that describe the flow of signaling information within a specific cell type.