Robust Perron cluster analysis in conformation dynamics

2004/12/24 by Peter Deuflhard, Marcus Weber · 532 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Mathematics · #Applied mathematics #Cluster (spacecraft) #Computer science #Discretization #Dynamical systems theory #Invariant (physics) #Markov Chains and Monte Carlo Methods #Markov chain #Mass Spectrometry Techniques and Applications #Mathematical analysis #Mathematical physics #Mathematics #Matrix (chemical analysis) #Metastability #Operator (biology) #Protein Structure and Dynamics

paper · pdf · doi:10.1016/j.laa.2004.10.026

published in Linear Algebra and its Applications 398, 161-184 (Elsevier BV)

openalex publication_date 2004/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

The key to molecular conformation dynamics is the direct identification of metastable conformations, which are almost invariant sets of molecular dynamical systems. Once some reversible Markov operator has been discretized, a generalized symmetric stochastic matrix arises. This matrix can be treated by Perron cluster analysis, a rather recent method involving a Perron cluster eigenproblem. The paper presents an improved Perron cluster analysis algorithm, which is more robust than earlier suggestions. Numerical examples are included.

Citations

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