Set theory formulation of the model-free problem and the diffusion seeded model-free paradigm

被引:21
|
作者
d'Auvergne, Edward J. [1 ]
Gooley, Paul R.
机构
[1] Univ Melbourne, Bio21 Inst Biotechnol & Mol Sci, Dept Biochem & Mol Biol, Melbourne, Vic 3010, Australia
[2] Univ Melbourne, Bio21 Inst Biotechnol & Mol Sci, Dept Biochem & Mol Biol, Parkville, Vic 3052, Australia
关键词
D O I
10.1039/b702202f
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Model-free analysis of NMR relaxation data, which describes the motion of individual atoms, is a problem intricately linked to the Brownian rotational diffusion of the macromolecule. The diffusion tensor parameters strongly influence the optimisation of the various model-free models and the subsequent model selection between them. Finding the optimal model of the dynamics of the system among the numerous diffusion and model- free models is hence quite complex. Using set theory, the entirety of this global problem has been encapsulated by the universal set, and its resolution mathematically formulated as the universal solution. Ever since the original Lipari and Szabo papers the model-free dynamics of a molecule has most often been solved by initially estimating the diffusion tensor. The model- free models which depend on the diffusion parameter values are then optimised and the best model is chosen to represent the dynamics of the residue. Finally, the global model of all diffusion and model-free parameters is optimised. These steps are repeated until convergence. For simplicity this approach to will be labelled the diffusion seeded model- free paradigm. Although this technique suffers from a number of problems many have been solved. All aspects of the diffusion seeded paradigm and its consequences, together with a few alternatives to the paradigm, will be reviewed through the use of set notation.
引用
收藏
页码:483 / 494
页数:12
相关论文
共 50 条
  • [1] MODEL-FREE CONTROL OF A SEEDED BATCH CRYSTALLIZER
    Afsi, Nawel
    Bakir, Toufik
    Othman, Sami
    Sakly, Anis
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2018, 96 (06): : 1306 - 1316
  • [2] Model-Free or Not?
    Zumpfe, Kai
    Smith, Albert A.
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2021, 8
  • [3] A MODEL-FREE ELASTICITY THEORY OF MELTING
    TALLON, JL
    ROBINSON, WH
    PHYSICS LETTERS A, 1982, 87 (07) : 365 - 368
  • [4] Model-Free Model Reconciliation
    Sreedharan, Sarath
    Hernandez, Alberto Olmo
    Mishra, Aditya Prasad
    Kambhampati, Subbarao
    PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2019, : 587 - 594
  • [5] Model-free CPPI
    Schied, Alexander
    JOURNAL OF ECONOMIC DYNAMICS & CONTROL, 2014, 40 : 84 - 94
  • [6] Model-free sampling
    Beer, Michael
    STRUCTURAL SAFETY, 2007, 29 (01) : 49 - 65
  • [7] Model-free control
    Fliess, Michel
    Join, Cedric
    INTERNATIONAL JOURNAL OF CONTROL, 2013, 86 (12) : 2228 - 2252
  • [8] Model-free metacognition
    Carruthers, Peter
    Williams, David M.
    COGNITION, 2022, 225
  • [9] Model-free Optimization: The Exploration-Exploitation Paradigm
    Raphel, Mariya
    Gunjal, Revati
    Wagh, S. R.
    Singh, N. M.
    2022 EIGHTH INDIAN CONTROL CONFERENCE, ICC, 2022, : 422 - 427
  • [10] Cooperative Adaptive Model-Free Control With Model-Free Estimation and Online Gain Tuning
    Safaei, Ali
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (09) : 8642 - 8654