Rule-based Modeling of Cell Signaling: Advances in Model Construction, Visualization and Simulation

摘要

Rule-based modeling is a graph-based approach to specifying the kinetics of cell signalingudsystems. A reaction rule is a compact and explicit graph-based representation of a kinetic process,udand it matches a class of reactions that involve identical sites and identical kinetics. Compact rule-udbased models have been used to generate large and combinatorially complex reaction networks,udand rules have also been used to compile databases of kinetic interactions targeting specific cellsudand pathways. In this work, I address three technological challenges associated with rule-basedudmodeling. First, I address the ability to generate an automated global visualization of a rule-basedudmodel as a network of signal flows. I showed how to analyze a reaction rule and extract a set ofudbipartite regulatory relationships, which can be aggregated across rules into a global network. Iudalso provide a set of coarse-graining approaches to compress an automatically generated networkudinto a compact pathway diagram, even for models with 100s of rules. Second, I resolved anudincompatibility between two recent advances in rule-based modeling: network-free simulationud(which enables simulation without generating a reaction network), and energy-based rule-basedudmodeling (which enables specifying a model using cooperativity parameters and automatedudaccounting of free energy). The incompatibility arose because calculating the reaction rate requiresudcomputing the reaction free energy in an energy-based model, and this requires knowledge of bothudreactants and products of the reaction, but the products are not available in a network-freeudsimulation until after the reaction event has fired. This was resolved by expanding each energy-udbased rule into a number of normal reaction rules for which reaction free energies can be calculatedudunambiguously. Third, I demonstrated a particular type of modularization that is based on treatinguda set of rules as a module. This enables building models from combinations of modular hypothesesudand supplements the other modularization strategies such as macros, types and energy-basedudcompression.