title: Model calibration and uncertainty analysis in signaling networks.
abstract: For a long time the biggest challenges in modeling cellular signal transduction networks has been the inference of crucial pathway components and the qualitative description of their interactions. As a result of the emergence of powerful high-throughput experiments, it is now possible to measure data of high temporal and spatial resolution and to analyze signaling dynamics quantitatively. In addition, this increase of high-quality data is the basis for a better understanding of model limitations and their influence on the predictive power of models. We review established approaches in signal transduction network modeling with a focus on ordinary differential equation models as well as related developments in model calibration. As central aspects of the calibration process we discuss possibilities of model adaptation based on data-driven parameter optimization and the concomitant objective of reducing model uncertainties.
authors.affiliation: Merrimack, One Kendall Sq., Suite B7201, Cambridge, MA 02139, USA.
authors.forename: Tim
authors.initials: T
authors.lastname: Heinemann
authors.affiliation: Merrimack, One Kendall Sq., Suite B7201, Cambridge, MA 02139, USA. Electronic address: araue@merrimack.com.
authors.forename: Andreas
authors.initials: A
authors.lastname: Raue
mesh_terms: D000818:Animals; D002138:Calibration; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D006801:Humans; D053858:Metabolic Networks and Pathways; D008962:Models, Theoretical; D015398:Signal Transduction; D035501:Uncertainty