Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study
KAUST Grant NumberKUK-C1-013-04
Online Publication Date2015-12-16
Print Publication Date2016
Permanent link to this recordhttp://hdl.handle.net/10754/623562
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AbstractThe last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.
CitationMacLean AL, Harrington HA, Stumpf MPH, Byrne HM (2016) Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study. Systems Medicine: 405–439. Available: http://dx.doi.org/10.1007/978-1-4939-3283-2_18.
SponsorsAll authors acknowledge funding from King Abdullah University of Science and Technology (KAUST) KUK-C1-013-04 and the workshop funded by this grant on Model Identification (January 2014). HAH gratefully acknowledges funding from EPSRC Fellowship EP/K041096/1. All authors also thank Gary Mirams for his help with Figs.4 and 5.
JournalMethods in Molecular Biology