title: Nonlinear dependencies of biochemical reactions for context-specific signaling dynamics.
abstract: Mathematical modeling can provide unique insights and predictions about a signaling pathway. Parameter variations allow identification of key reactions that govern signaling features such as the response time that may have a direct impact on the functional outcome. The effect of varying one parameter, however, may depend on values of another. To address the issue, we performed multi-parameter variations of an experimentally validated mathematical model of NF-κB regulatory network, and analyzed the inter-relationships of the parameters in shaping key dynamic features. We find that nonlinear dependencies are ubiquitous among parameters. Such phenomena may underlie the emergence of cell type-specific behaviors from essentially the same molecular network. Our results from a multivariate ensemble of models highlight the hypothesis that cell type specificity in signaling phenotype can arise from quantitatively altered strength of reactions in the pathway, in the absence of tissue-specific factors that re-wire the network for a new topology.
authors.affiliation: Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health , Bethesda, MD 20892, USA. sungm@mail.nih.gov
authors.forename: Myong-Hee
authors.initials: MH
authors.lastname: Sung
authors.forename: Gordon L
authors.initials: GL
authors.lastname: Hager
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020672:I-kappa B Proteins / Q000502:physiology; D007700:Kinetics; D008954:Models, Biological; D015999:Multivariate Analysis; D016328:NF-kappa B / Q000502:physiology; D017711:Nonlinear Dynamics; D060066:Protein Interaction Maps; D015398:Signal Transduction
chemical_list: D020672:I-kappa B Proteins; D016328:NF-kappa B