scholarly journals Toolbox for quantifying memory in dynamics along reaction coordinates

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Alessio Lapolla ◽  
Aljaž Godec
Keyword(s):  
Author(s):  
Michael P. Allen ◽  
Dominic J. Tildesley

The development of techniques to simulate infrequent events has been an area of rapid progress in recent years. In this chapter, we shall discuss some of the simulation techniques developed to study the dynamics of rare events. A basic summary of the statistical mechanics of barrier crossing is followed by a discussion of approaches based on the identification of reaction coordinates, and those which seek to avoid prior assumptions about the transition path. The demanding technique of transition path sampling is introduced and forward flux sampling and transition interface sampling are considered as rigorous but computationally efficient approaches.


2020 ◽  
Vol 31 (1) ◽  
Author(s):  
Andreas Bittracher ◽  
Stefan Klus ◽  
Boumediene Hamzi ◽  
Péter Koltai ◽  
Christof Schütte

AbstractWe present a novel kernel-based machine learning algorithm for identifying the low-dimensional geometry of the effective dynamics of high-dimensional multiscale stochastic systems. Recently, the authors developed a mathematical framework for the computation of optimal reaction coordinates of such systems that is based on learning a parameterization of a low-dimensional transition manifold in a certain function space. In this article, we enhance this approach by embedding and learning this transition manifold in a reproducing kernel Hilbert space, exploiting the favorable properties of kernel embeddings. Under mild assumptions on the kernel, the manifold structure is shown to be preserved under the embedding, and distortion bounds can be derived. This leads to a more robust and more efficient algorithm compared to the previous parameterization approaches.


2018 ◽  
Vol 149 (15) ◽  
pp. 154103 ◽  
Author(s):  
Andreas Bittracher ◽  
Ralf Banisch ◽  
Christof Schütte

2010 ◽  
Vol 98 (9) ◽  
pp. 1911-1920 ◽  
Author(s):  
Ernesto E. Borrero ◽  
Lydia M. Contreras Martínez ◽  
Matthew P. DeLisa ◽  
Fernando A. Escobedo

2018 ◽  
Vol 122 (46) ◽  
pp. 9031-9042 ◽  
Author(s):  
Jonathan D. Handali ◽  
Kyle F. Sunden ◽  
Blaise J. Thompson ◽  
Nathan A. Neff-Mallon ◽  
Emily M. Kaufman ◽  
...  

2020 ◽  
pp. 247255522095838
Author(s):  
Maria Filipa Pinto ◽  
Francisco Figueiredo ◽  
Alexandra Silva ◽  
António R. Pombinho ◽  
Pedro José Barbosa Pereira ◽  
...  

The throughput level currently reached by automatic liquid handling and assay monitoring techniques is expected to facilitate the discovery of new modulators of enzyme activity. Judicious and dependable ways to interpret vast amounts of information are, however, required to effectively answer this challenge. Here, the 3-point method of kinetic analysis is proposed as a means to significantly increase the hit success rates and decrease the number of falsely identified compounds (false positives). In this post-Michaelis–Menten approach, each screened reaction is probed in three different occasions, none of which necessarily coincide with the initial period of constant velocity. Enzymology principles rather than subjective criteria are applied to identify unwanted outliers such as assay artifacts, and then to accurately distinguish true enzyme modulation effects from false positives. The exclusion and selection criteria are defined based on the 3-point reaction coordinates, whose relative positions along the time-courses may change from well to well or from plate to plate, if necessary. The robustness and efficiency of the new method is illustrated during a small drug repurposing screening of potential modulators of the deubiquinating activity of ataxin-3, a protein implicated in Machado–Joseph disease. Apparently, intractable Z factors are drastically enhanced after (1) eliminating spurious results, (2) improving the normalization method, and (3) increasing the assay resilience to systematic and random variability. Numerical simulations further demonstrate that the 3-point analysis is highly sensitive to specific, catalytic, and slow-onset modulation effects that are particularly difficult to detect by typical endpoint assays.


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