  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.
