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SUMMARY:Multiscale methods for modelling intracellular processes
DTSTART;VALUE=DATE-TIME:20180709T010000Z
DTEND;VALUE=DATE-TIME:20180709T013000Z
DTSTAMP;VALUE=DATE-TIME:20241102T165511Z
UID:indico-contribution-20@conferences.maths.unsw.edu.au
DESCRIPTION:Speakers: Radek Erban (University of Oxford)\nI will discuss m
athematical and computational methods for spatio-temporal modelling in mol
ecular and cell biology\, including all-atom and coarse-grained molecular
dynamics (MD)\, Brownian dynamics (BD)\, stochastic reaction-diffusion mod
els and macroscopic mean-field equations. \n\nMicroscopic (BD\, MD) models
are based on the simulation of trajectories of individual molecules and t
heir localized interactions (for example\, reactions). Mesoscopic (lattice
-based) stochastic reaction-diffusion approaches divide the computational
domain into a finite number of compartments and simulate the time evolutio
n of the numbers of molecules in each compartment\, while macroscopic mode
ls are often written in terms of mean-field reaction-diffusion partial dif
ferential equations for spatially varying concentrations. \n\nI will discu
ss the development\, analysis and applications of multiscale methods for s
patio-temporal modelling of intracellular processes\, which use (detailed)
BD or MD simulations in localized regions of particular interest (in whic
h accuracy and microscopic details are important) and a (less-detailed) co
arser model in other regions in which accuracy may be traded for simulatio
n efficiency [1\,2\,3]. I will discuss error analysis and convergence prop
erties of the developed multiscale methods\, their software implementation
[4] and applications of these multiscale methodologies to modelling of in
tracellular calcium dynamics [5]\, actin dynamics [6\,7] and DNA dynamics
[8]. I will also discuss the development of multiscale methods which coupl
e MD and coarser stochastic models in the same dynamic simulation [3\,9].\
n \n[1] M. Flegg\, S.J. Chapman and R. Erban (2012). Two Regime Method for
optimizing stochastic reaction-diffusion simulations. *Journal of the Roy
al Society Interface* **9**: 859-868.\n[2] B. Franz\, M. Flegg\, S.J. Chap
man and R. Erban (2013). Multiscale reaction-diffusion algorithms: PDE-ass
isted Brownian dynamics. *SIAM Journal on Applied Mathematics* **73**: 122
4-1247.\n[3] R. Erban (2014). From molecular dynamics to Brownian dynamics
. *Proceedings of the Royal Society A* **470**: 20140036.\n[4] M. Robinson
\, S. Andrews and R. Erban (2015). Multiscale reaction-diffusion simulatio
ns with Smoldyn. *Bioinformatics* **31**: 2406-2408. \n[5] U. Dobramysl\,
S. Rudiger and R. Erban (2016). Particle-based multiscale modeling of calc
ium puff dynamics. *Multiscale Modelling and Simulation* **14**: 997-1016.
\n[6] R. Erban\, M. Flegg and G. Papoian (2014). Multiscale stochastic r
eaction-diffusion modelling: application to actin dynamics in filopodia. *
Bulletin of Mathematical Biology* **76**: 799-818.\n[7] U. Dobramysl\, G.
Papoian and R. Erban (2016). Steric effects induce geometric remodeling of
actin bundles in filopodia. *Biophysical Journal* **110**: 2066-2075. \n[
8] E. Rolls\, Y. Togashi and R. Erban (2017). Varying the resolution of th
e Rouse model on temporal and spatial scales: application to multiscale mo
delling of DNA dynamics. *Multiscale Modelling and Simulation* **15**(4):
1672-1693.\n[9] R. Erban (2016). Coupling all-atom molecular dynamics simu
lations of ions in water with Brownian dynamics. *Proceedings of the Royal
Society A* **472**: 20150556.\n\nhttps://conferences.maths.unsw.edu.au/ev
ent/2/contributions/20/
LOCATION:University of Sydney New Law School/--100
URL:https://conferences.maths.unsw.edu.au/event/2/contributions/20/
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