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

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.