Index analysis: an approach to understand signal transduction with application to the EGFR signalling pathway

TL;DR

This paper introduces index analysis, a method to identify key molecular species and dynamic properties in complex signal transduction models, demonstrated on the EGFR pathway to improve understanding and modeling.

Contribution

It presents a novel index analysis approach for analyzing large-scale kinetic models, aiding in identifying important species and dynamic phases in signal transduction.

Findings

Identified phases of signal transduction in EGFR pathway

Highlighted the role of Phosphatase3 during activation

Revealed Ras recycling during signal onset

Abstract

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. We envision that index analysis will be beneficial in comparing different model scenarios (e.g., healthy and diseased conditions), in designing more informed model reduction approaches, and in translating large-scale systems biology models from early to late phase in drug discovery and development.