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SUMMARY: Modelling of equilibrium and non-equilibrium time-series data: fr
 om protein folding to weather forecasting - Roland R. Netz (Department of 
 Physics\, Free University Berlin\, Arnimallee 14\, 14195 Berlin\, Germany)
DTSTART:20240423T120000Z
DTEND:20240423T130000Z
UID:TALK209758@talks.cam.ac.uk
CONTACT:Sarah Loos
DESCRIPTION:Most systems of scientific interest are interacting many-body 
 systems. One typically describes their kinetics in terms of a low-dimensio
 nal reaction coordinate\, which in general is influenced by the entire sys
 tem. The dynamics of such a reaction coordinate is governed by the general
 ized Langevin equation (GLE)\, an integro-differential stochastic equation
 \, and involves a memory function [1]. I discuss a few examples where the 
 GLE can be used to interpret and model data in different fields of science
 .\n\nProtein-folding kinetics is typically described as Markovian (i.e.\, 
 memoryless) diffusion in a one-dimensional free energy landscape. By analy
 sis of large-scale molecular-dynamics simulation trajectories of fast-fold
 ing proteins from the Shaw group using the special-purpose computer ANTON\
 , I demonstrate that the friction characterizing protein folding exhibits 
 significant memory with a decay time that is of the same order as the fold
 ing and unfolding times [2\,3]. Memory friction effects lead to anomalous 
 and drastically modified protein kinetics. For the set of proteins for whi
 ch simulations are available\, it is shown that the folding and unfolding 
 times are not dominated by the free-energy barrier but rather by the non-M
 arkovian friction.\n\nMemory effects are also present for non-equilibrium 
 systems. Using an appropriate non-equilibrium formulation of the GLE\, it 
 is demonstrated that the motion of living organisms is characterized by me
 mory friction\, which allows to characterize internal feedback loops of su
 ch organisms and to classify and sort individual organisms [4]. The GLE ca
 n be even used to predict complex phenomena such as weather data.\n\n[1] G
 eneralized Langevin equation with a nonlinear potential of mean force and 
 nonlinear memory friction from a hybrid projection scheme\nCihan Ayaz \, L
 aura Scalfi \, Benjamin A. Dalton\, and Roland R. Netz\nPHYSICAL REVIEW E 
 105\, 054138 (2022)\n\n[2] Non-Markovian modeling of protein folding\,\nCi
 han Ayaza\, Lucas Tepper\, Florian N. Brünig\, Julian Kappler\, Jan O. Da
 ldrop\, Roland R. Netz\nProc. Natl Acad. Sci. 118\,  e2023856118 (2021)\n\
 n[4] Fast protein folding is governed by memory-dependent friction\nBenjam
 in A. Dalton\, Cihan Ayaz\, Lucas Tepper\, and Roland R. Netz.\nProc. Natl
  Acad. Sci. 120\, e2220068120 (2023)\, DOI: 10.1073/pnas.2220068120\n\n[4]
  Data-driven classification of individual cells by their non-Markovian mot
 ion\nAnton Klimek\, Debasmita Mondal\, Stephan Block\, Prerna Sharma\, and
  Roland R. Netz \nBiophysical Journal 123\, 1–11\, May 7\, 2024\, https:
 //doi.org/10.1016/j.bpj.2024.03.023\n
LOCATION:Center for Mathematical Sciences\, Lecture room MR4
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