title: How spatial heterogeneity shapes multiscale biochemical reaction network dynamics.
abstract: Spatial heterogeneity in cells can be modelled using distinct compartments connected by molecular movement between them. In addition to movement, changes in the amount of molecules are due to biochemical reactions within compartments, often such that some molecular types fluctuate on a slower timescale than others. It is natural to ask the following questions: how sensitive is the dynamics of molecular types to their own spatial distribution, and how sensitive are they to the distribution of others? What conditions lead to effective homogeneity in biochemical dynamics despite heterogeneity in molecular distribution? What kind of spatial distribution is optimal from the point of view of some downstream product? Within a spatially heterogeneous multiscale model, we consider two notions of dynamical homogeneity (full homogeneity and homogeneity for the fast subsystem), and consider their implications under different timescales for the motility of molecules between compartments. We derive rigorous results for their dynamics and long-term behaviour, and illustrate them with examples of a shared pathway, Michaelis-Menten enzymatic kinetics and autoregulating feedbacks. Using stochastic averaging of fast fluctuations to their quasi-steady-state distribution, we obtain simple analytic results that significantly reduce the complexity and expedite simulation of stochastic compartment models of chemical reactions.
authors: Abteilung fur Mathematische Stochastik, Eckerstrasse 1,  79104 Freiburg, Germany p.p@stochastik.uni-freiburg.de.
authors: Peter
authors: P
authors: Pfaffelhuber
authors: Department of Mathematics and Statistics, Concordia University, Montreal, Quebec, Canada H3G 1M8.
authors: Lea
authors: L
authors: Popovic
mesh_terms: D000465:Algorithms; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D007700:Kinetics; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D012333:RNA, Messenger / Q000378:metabolism; D015398:Signal Transduction; D013269:Stochastic Processes
chemical_list: D012333:RNA, Messenger
keywords: compartment model; model reduction; multiple timescales; quasi-steady state assumption; scaling limits; stochastic averaging
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