Decision-tree based model analysis for efficient identification of parameter relations leading to different signaling states.

ABSTRACT: In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a no

SUBMITTER: Koch Y 

PROVIDER: S-EPMC3867358 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature