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.