Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Applications of Membrane Computing in Systems and Synthetic BiologyMolecular Diffusion and Compartmentalization in Signal Transduction Pathways: An Application of Membrane Systems to the Study of Bacterial Chemotaxis

Applications of Membrane Computing in Systems and Synthetic Biology: Molecular Diffusion and... [In this chapter we present an application of membrane systems to the study of intracellular diffusive processes. In particular, a class of membrane systems, called τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau $$\end{document}-DPP, is used for the modeling, simulation and analysis of bacterial chemotaxis. Two different models of this signal transduction pathway are presented. The first is a single volume model used to investigate the properties of bacterial chemotaxis and to analyze the effects of different perturbations (deletion of chemotactic proteins, addition of distinct amounts of external ligand, effect of different methylation states of the receptors) on the system dynamics. The second model represents a multivolume extension of the former, and it is exploited for the analysis of the diffusive processes that give rise to the formation of concentration gradients throughout the bacterial cytoplasm. The outcome of stochastic simulations of both models are exploited to analyze the process of synchronization of flagella, in order to evaluate the running and tumbling time intervals of bacterial cells.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Applications of Membrane Computing in Systems and Synthetic BiologyMolecular Diffusion and Compartmentalization in Signal Transduction Pathways: An Application of Membrane Systems to the Study of Bacterial Chemotaxis

Part of the Emergence, Complexity and Computation Book Series (volume 7)
Editors: Frisco, Pierluigi; Gheorghe, Marian; Pérez-Jiménez, Mario J.
Cazzaniga, Paolo; Besozzi, Daniela; Pescini, Dario; Mauri, Giancarlo
Applications of Membrane Computing in Systems and Synthetic Biology — Dec 18, 2013
Download PDF
32 pages

Loading next page...
 
/lp/springer-journals/applications-of-membrane-computing-in-systems-and-synthetic-biology-TM9RB96NQP
Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2014
ISBN
978-3-319-03190-3
Pages
65 –96
DOI
10.1007/978-3-319-03191-0_3
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter we present an application of membrane systems to the study of intracellular diffusive processes. In particular, a class of membrane systems, called τ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau $$\end{document}-DPP, is used for the modeling, simulation and analysis of bacterial chemotaxis. Two different models of this signal transduction pathway are presented. The first is a single volume model used to investigate the properties of bacterial chemotaxis and to analyze the effects of different perturbations (deletion of chemotactic proteins, addition of distinct amounts of external ligand, effect of different methylation states of the receptors) on the system dynamics. The second model represents a multivolume extension of the former, and it is exploited for the analysis of the diffusive processes that give rise to the formation of concentration gradients throughout the bacterial cytoplasm. The outcome of stochastic simulations of both models are exploited to analyze the process of synchronization of flagella, in order to evaluate the running and tumbling time intervals of bacterial cells.]

Published: Dec 18, 2013

Keywords: Methylation State; Stochastic Simulation; Stochastic Fluctuation; Stochastic Simulation Algorithm; Bacterial Chemotaxis

There are no references for this article.