1. Information Content in Data Sets for a Nucleated-Polymerization Model
- Author
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Harvey Thomas Banks, Marie Doumic, Carola Kruse, Stéphanie Prigent, Human Rezaei, Center for Research in Scientific Computation [Raleigh] (CRSC), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), Modelling and Analysis for Medical and Biological Applications (MAMBA), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Unité de recherche Virologie et Immunologie Moléculaires (VIM), Institut National de la Recherche Agronomique (INRA), MAMBA, European Project: 306321,EC:FP7:ERC,ERC-2012-StG_20111012,SKIPPERAD(2012), and Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892))
- Subjects
FOS: Computer and information sciences ,Inverse problems ,Amyloid ,Protein polymerization ,Computer science ,Process (engineering) ,nucleation and growth kinetics ,uncertainty quantification ,Context (language use) ,Models, Biological ,Sensitivity and Specificity ,Statistics - Applications ,information content ,Article ,Polymerization ,Computational Engineering, Finance, and Science (cs.CE) ,Mathematics - Analysis of PDEs ,Polyglutamine aggregates ,FOS: Mathematics ,[MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP] ,Applications (stat.AP) ,Sensitivity (control systems) ,Uncertainty quantification ,Computer Science - Computational Engineering, Finance, and Science ,protein polymerization ,Ecology, Evolution, Behavior and Systematics ,Bootstrapping (statistics) ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Models, Statistical ,Ecology ,Amyloid fibrils models ,Statistical model ,[INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] ,Fisher matrix ,Kinetics ,Sensitivity Analysis ,Content (measure theory) ,65M32,62P10,64B10,49Q12 ,Peptides ,Algorithm ,Algorithms ,Analysis of PDEs (math.AP) - 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.
- Published
- 2015
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