Title: An Egf Signaling RoadMap in Pathway Logic Authors: Merrill Knapp, Carolyn Talcott, and Keith Laderoute Corresponding Author: Carolyn Talcott Poster Abstract: Pathway Logic is an approach to modeling cellular processes based on rewriting logic, a simple logic designed for modeling and analysis of distributed systems. It allows one to model aspects of the structure and state of interacting components as elements of an abstract data type; to represent individual process steps (reactions) as rewrite rules; and to study possible ways a system might evolve using techniques based on logical inference. Given a network of reactions and a specification of cellular components one can query the network about possible reaction pathways and outcomes. Knockouts that prevent a given outcome can be computed, competing reactions can be found, and pathways can be compared to look for potential cross-talk. Epidermal growth factor receptor (EgfR) signaling regulates growth, survival, proliferation, and differentiation in mammalian cells. We present a Pathway Logic model of early response to Epidermal growth factor (Egf) stimulation in adherent cells expressing Egf-receptors. The model is entirely based on experimental results and data curated from the published scientific literature. We explain how biochemical reactions or processes are represented as rewrite rules, as well as how the curation process determines the components of a reaction rule by extracting information about state changes and biological context from experimental data to determine the components of a reaction rule. A reaction network was assembled from data supporting events that might occur downstream of EgfR signaling; the network has over 370 reactions involving more than 460 species (signaling molecules in different states and locations). This network was constrained to reflect outcomes that have been experimentally demonstrated to occur in response to a short stimulus with Egf. So far more than 80 such outcomes have been collected and used to test the adequacy of the model---that is, does it predict these observed outcomes? We will also point out some surprises that appear in the generated pathways because of the richness of the biological context represented in the curated model. Pathway Logic models are qualitative and thus answer different kinds of questions than quantitative differential equation-based or stochastic models. They can also serve as useful road maps for developing quantitative models of subsystems and understanding the potential cross talk between signaling submodules.