title: A guide to sensitivity analysis of quantitative models of gene expression dynamics.
abstract: 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.
authors.affiliation: School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Bradford
authors.initials: B
authors.lastname: Taylor
authors.forename: Tae J
authors.initials: TJ
authors.lastname: Lee
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Weitz
mesh_terms: D010582:Bacteriophage lambda / Q000235:genetics* / Q000378:metabolism; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000821:virology; D025461:Feedback, Physiological; D015967:Gene Expression Regulation, Viral; D008242:Lysogeny / Q000235:genetics; D008957:Models, Genetic; D012333:RNA, Messenger / Q000235:genetics* / Q000378:metabolism; D012680:Sensitivity and Specificity; D012984:Software; D035501:Uncertainty; D014764:Viral Proteins / Q000235:genetics* / Q000378:metabolism
chemical_list: D012333:RNA, Messenger; D014764:Viral Proteins
keywords: Bacteria; Differential equations; Gene regulation; Intracellular dynamics; Statistics; Virus