title: Information content in data sets for a nucleated-polymerization model.
abstract: We illustrate the use of statistical tools (asymptotic theories of standard error quantification using appropriate statistical models, bootstrapping, and model comparison techniques) in addition to sensitivity analysis that may be employed to determine the information content in data sets. We do this in the context of recent models [S. Prigent, A. Ballesta, F. Charles, N. Lenuzza, P. Gabriel, L.M. Tine, H. Rezaei, and M. Doumic, An efficient kinetic model for assemblies of amyloid fibrils and its application to polyglutamine aggregation, PLoS ONE 7 (2012), e43273. doi:10.1371/journal.pone.0043273.] for nucleated polymerization in proteins, about which very little is known regarding the underlying mechanisms; thus, the methodology we develop here may be of great help to experimentalists. We conclude that the investigated data sets will support with reasonable levels of uncertainty only the estimation of the parameters related to the early steps of the aggregation process.
authors.affiliation: a Center for Research in Scientific Computation , North Carolina State University , Raleigh , NC 27695-8212 , USA.
authors.forename: H T
authors.initials: HT
authors.lastname: Banks
authors.forename: Marie
authors.initials: M
authors.lastname: Doumic
authors.forename: Carola
authors.initials: C
authors.lastname: Kruse
authors.forename: Stephanie
authors.initials: S
authors.lastname: Prigent
authors.forename: Human
authors.initials: H
authors.lastname: Rezaei
mesh_terms: D000465:Algorithms; D000682:Amyloid / Q000737:chemistry*; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry*; D058105:Polymerization; D012680:Sensitivity and Specificity
chemical_list: D000682:Amyloid; D010455:Peptides; C097188:polyglutamine
keywords: 49Q12; 62P10; 64B10; 65M32; information content; inverse problems; polyglutamine and aggregation modelling; sensitivity; uncertainty quantification