A guide to sensitivity analysis of quantitative models of gene expression dynamics

• Published in: Methods (San Diego, Calif.) Vol. 62; no. 1; pp. 109 - 120
• Main Authors: Taylor, Bradford, Lee, Tae J., Weitz, Joshua S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.07.2013
• Subjects: Bacteria; Bacteriophage lambda - genetics; Bacteriophage lambda - metabolism; Data Interpretation, Statistical; Differential equations; Escherichia coli - virology; Feedback, Physiological; Gene Expression Regulation, Viral; Gene regulation; Intracellular dynamics; Lysogeny - genetics; Models, Genetic; RNA, Messenger - genetics; RNA, Messenger - metabolism; Sensitivity and Specificity; Software; Statistics; Uncertainty; Viral Proteins - genetics; Viral Proteins - metabolism; Virus; Virus; Bacteria; Statistics; Gene regulation; Intracellular dynamics; Differential equations
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2013.03.007

We provide a guide to performing a sensitivity analysis (SA) of quantitative models of gene expression dynamics appropriate to the levels of uncertainty in the model: spanning cases where parameters are relatively well-constrained to cases where they are poorly constrained. In the well-constrained case, we present methods to perform “local” SA (LSA), which considers small perturbations for a single set of model parameter values. In the poorly-constrained case, we present methods to perform “global” SA (GSA) as a means to evaluate the sensitivity of a model over large regions of parameter space. We apply these methods to quantitative models of increasing complexity. The models we consider are simple logistic growth, negative feedback in a mRNA–protein model, and two models of decision making within bacteriophage λ. We discuss the best practices for how SA can be utilized in an iterative fashion to advance biological understanding.