Information Content in Data Sets for a Nucleated-Polymerization Model

H. T. Banks (1) , Marie Doumic (2, 3) , Carola Kruse (2, 3) , Stephanie Prigent (2, 3) , H Rezaei (4)

1 CRSC - Center for Research in Scientific Computation [Raleigh]
2 MAMBA - Modelling and Analysis for Medical and Biological Applications
3 LJLL - Laboratoire Jacques-Louis Lions
4 VIM (UR 0892) - Unité de recherche Virologie et Immunologie Moléculaires

We illustrate the use of tools (asymptotic theories of standard error quantification using appropriate statistical models, bootstrapping, model comparison techniques) in addition to sensitivity that may be employed to determine the information content in data sets. We do this in the context of recent models [23] 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.

Keywords: Polyglutamine aggregates; information content; nucleation and growth kinetics; Amyloid fibrils models; protein polymerization; Fisher matrix; uncertainty quantification; Inverse problems; Sensitivity Analysis

Domains: Analysis of PDEs [math.AP]; Applications [stat.AP]; Numerical Analysis [cs.NA]

H. T. Banks, Marie Doumic, Carola Kruse, Stephanie Prigent, H Rezaei. Information Content in Data Sets for a Nucleated-Polymerization Model. Journal of Biological Dynamics, 2015, 9 (1), pp.26. ⟨10.1080/17513758.2015.1050465⟩. ⟨hal-01123847⟩