Literature DB >> 25652460

How spatial heterogeneity shapes multiscale biochemical reaction network dynamics.

Peter Pfaffelhuber1, Lea Popovic2.   

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.
© 2015 The Author(s) Published by the Royal Society. All rights reserved.

Keywords:  compartment model; model reduction; multiple timescales; quasi-steady state assumption; scaling limits; stochastic averaging

Mesh:

Substances:

Year:  2015        PMID: 25652460      PMCID: PMC4345478          DOI: 10.1098/rsif.2014.1106

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  19 in total

1.  A new method for choosing the computational cell in stochastic reaction-diffusion systems.

Authors:  Hye-Won Kang; Likun Zheng; Hans G Othmer
Journal:  J Math Biol       Date:  2011-11-10       Impact factor: 2.259

2.  The two-regime method for optimizing stochastic reaction-diffusion simulations.

Authors:  Mark B Flegg; S Jonathan Chapman; Radek Erban
Journal:  J R Soc Interface       Date:  2011-10-19       Impact factor: 4.118

Review 3.  Noise in gene expression: origins, consequences, and control.

Authors:  Jonathan M Raser; Erin K O'Shea
Journal:  Science       Date:  2005-09-23       Impact factor: 47.728

4.  Stochastic simulation of chemical reactions with spatial resolution and single molecule detail.

Authors:  Steven S Andrews; Dennis Bray
Journal:  Phys Biol       Date:  2004-12       Impact factor: 2.583

Review 5.  Kinetics of bimolecular reactions in model bilayers and biological membranes. A critical review.

Authors:  Eurico Melo; Jorge Martins
Journal:  Biophys Chem       Date:  2006-05-30       Impact factor: 2.352

6.  Spatial partitioning improves the reliability of biochemical signaling.

Authors:  Andrew Mugler; Filipe Tostevin; Pieter Rein ten Wolde
Journal:  Proc Natl Acad Sci U S A       Date:  2013-03-25       Impact factor: 11.205

Review 7.  Lipid rafts as a membrane-organizing principle.

Authors:  Daniel Lingwood; Kai Simons
Journal:  Science       Date:  2010-01-01       Impact factor: 47.728

8.  Stochastic reaction-diffusion kinetics in the microscopic limit.

Authors:  David Fange; Otto G Berg; Paul Sjöberg; Johan Elf
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-01       Impact factor: 11.205

9.  Confining domains lead to reaction bursts: reaction kinetics in the plasma membrane.

Authors:  Ziya Kalay; Takahiro K Fujiwara; Akihiro Kusumi
Journal:  PLoS One       Date:  2012-03-27       Impact factor: 3.240

10.  A linear framework for time-scale separation in nonlinear biochemical systems.

Authors:  Jeremy Gunawardena
Journal:  PLoS One       Date:  2012-05-14       Impact factor: 3.240

View more
  1 in total

Review 1.  Systems Pharmacology in Small Molecular Drug Discovery.

Authors:  Wei Zhou; Yonghua Wang; Aiping Lu; Ge Zhang
Journal:  Int J Mol Sci       Date:  2016-02-18       Impact factor: 5.923

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.