Model Selection of Competing Modularized Regulatory Topologies of Signaling Pathways
EMSL Project ID
60156
Abstract
Genetic mutations among components of intracellular signaling pathways are often associated with cancer and such components are thus attractive therapeutic targets. Understanding these pathway alterations, the resulting changes in network dynamics, and their responses to extracellular signals is crucial to the development of effective pharmaceutical interventions. Unfortunately, signaling networks are complex and various pathways are highly intertwined leading to an inadequate understanding of pharmaceutical effects and typically disappointing outcomes from traditional experimental approaches to therapeutic targeting. Computational modeling and simulation can help facilitate a better understanding of network structure and the effects of perturbations of their components but the high complexity and the lack of contextual understanding of the interplay between pathways and pathway components limits our ability to build predictive models and elucidate the precise underlying mechanisms of the observed effects. Deficiencies in quantitative information for pathway component concentrations and reaction rate parameters further compounds the problem.
To build predictive models despite the lack of contextual and quantitative information we propose an approach that reduces complex mechanistic models into simpler coarse-grained models consisting of non-mechanistic modules that encompass highly connected portions of the network but are sparsely connected to other parts of the network. This approach will allow predictive analysis, hypothesis generation, and further model refinement. We have as our test system a modularized model of the EGFR-ERK pathway consisting of four species: ligand, phosphotyrosine, Ras, and Erk. These species act as the interconnects between the modules of the system: EGFR, Grb2-SOS, the ERK2 cascade, and ADAM17. The modularized portions of the network act as non-mechanistic but parameterizable transfer functions that connect the system species. In addition, there are regulatory functions on the modules with constant parameters and that vary only with the initial ligand. With additional computational resources we will extend the successful preliminary calibration results of this canonical model to model selection efforts using topological variants of its structure.
Project Details
Project type
Limited Scope
Start Date
2021-08-04
End Date
2021-10-03
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members