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Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
E-mail: a.kierzek@surrey.ac.uk
; Fax: +44 (0)1483686401
; Tel: 15 +44 (0)1483683775
b
Department of Biological Sciences, Purdue University, West Lafayette, USA
E-mail: blwanner@purdue.edu
; Fax: +1 (1)7654940876
; Tel: +1 (1)7654944408
Mol. BioSyst., 2010,6, 531-542
DOI:
10.1039/B906951H
Received
07 Apr 2009,
Accepted
22 Oct 2009
First published online
02 Dec 2009
Two-component systems (TCSs) are prevalent signal transduction systems in bacteria that control innumerable adaptive responses to environmental cues and host–pathogen interactions. We constructed a detailed stochastic kinetic model of two component signalling based on published data. Our model has been validated with flow cytometry data and used to examine reporter gene expression in response toextracellular signal strength. The model shows that, depending on the actual kinetic parameters, TCSs exhibit all-or-none, graded or mixed mode responses. In accordance with other studies, positively autoregulated TCSs exhibit all-or-none responses. Unexpectedly, our model revealed that TCSs lacking a positive feedback loop exhibit not only graded but also mixed mode responses, in which variation of the signal strength alters the level of gene expression in induced cells while the regulated gene continues to be expressed at the basal level in a substantial fraction of cells. The graded response of the TCS changes to mixed mode response by an increase of the translation initiation rate of the histidine kinase. Thus, a TCS is an evolvable design pattern capable of implementing deterministic regulation and stochastic switches associated with both graded and threshold responses. This has implications for understanding the emergence of population diversity in pathogenic bacteria and the design of genetic circuits in synthetic biology applications. The model is available in systems biology markup language (SBML) and systems biology graphical notation (SBGN) formats and can be used as a component of large-scale biochemical reaction network models.
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