NDLI: Ultrasensitivity in independent multisite systems

Content Provider	Springer Nature Link
Author	Ryerson, Shane Enciso, Germán A.
Copyright Year	2013
Abstract	Multisite modifications are widely recognized as an essential feature of many switch-like responses in signal transduction. It is usually assumed that the modification of one site directly or indirectly increases the rate of modification of neighboring sites. In this paper we provide a new set of assumptions for a multisite system to become highly ultrasensitive even in the absence of cooperativity or allostery. We assume that the individual sites are modified independently of each other, and that protein activity is an ultrasensitive function of the fraction of modified sites. These assumptions are particularly useful in the context of multisite systems with a large (8+) number of sites. We estimate the apparent Hill coefficient of the dose responses in the sequential and nonsequential cases, highlight their different qualitative properties, and discuss a formula to approximate dose responses in the nonsequential case. As an example we describe a model of bacterial chemotaxis that features robust ultrasensitivity and perfect adaptation over a wide range of ligand concentrations, based on non-allosteric multisite behavior at the level of receptors and flagella. We also include a model of the inactivation of the yeast pheromone protein Ste5 by cell cycle proteins.
Ending Page	999
Page Count	23
Starting Page	977
File Format	PDF
ISSN	03036812
e-ISSN	14321416
Journal	Journal of Mathematical Biology
Issue Number	4
Volume Number	69
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dynamical systems in biology Signal transduction Phosphorylation Bacterial chemotaxis Biochemistry, molecular biology Multisite system Mathematical and Computational Biology Ultrasensitivity Allostery Cooperativity Applications of Mathematics Systems biology, networks
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Agricultural and Biological Sciences Modeling and Simulation

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