From Qualitative Data to Quantitative Models: Analysis of the Phage Shock Protein Response in Escherichia coli
Author(s)
Toni, T
Jovanovic, G
Huvet, M
Buck, M
Type
Journal Article
Abstract
Background: Bacteria have evolved a rich set of mechanisms for sensing and adapting to adverse conditions in
their environment. These are crucial for their survival, which requires them to react to extracellular stresses such as
heat shock, ethanol treatment or phage infection. Here we focus on studying the phage shock protein (Psp) stress
response in Escherichia coli induced by a phage infection or other damage to the bacterial membrane. This system
has not yet been theoretically modelled or analysed in silico.
Results: We develop a model of the Psp response system, and illustrate how such models can be constructed and
analyzed in light of available sparse and qualitative information in order to generate novel biological hypotheses
about their dynamical behaviour. We analyze this model using tools from Petri-net theory and study its dynamical
range that is consistent with currently available knowledge by conditioning model parameters on the available
data in an approximate Bayesian computation (ABC) framework. Within this ABC approach we analyze stochastic
and deterministic dynamics. This analysis allows us to identify different types of behaviour and these mechanistic
insights can in turn be used to design new, more detailed and time-resolved experiments.
Conclusions: We have developed the first mechanistic model of the Psp response in E. coli. This model allows us
to predict the possible qualitative stochastic and deterministic dynamic behaviours of key molecular players in the
stress response. Our inferential approach can be applied to stress response and signalling systems more generally:
in the ABC framework we can condition mathematical models on qualitative data in order to delimit e.g.
parameter ranges or the qualitative system dynamics in light of available end-point or qualitative information.
their environment. These are crucial for their survival, which requires them to react to extracellular stresses such as
heat shock, ethanol treatment or phage infection. Here we focus on studying the phage shock protein (Psp) stress
response in Escherichia coli induced by a phage infection or other damage to the bacterial membrane. This system
has not yet been theoretically modelled or analysed in silico.
Results: We develop a model of the Psp response system, and illustrate how such models can be constructed and
analyzed in light of available sparse and qualitative information in order to generate novel biological hypotheses
about their dynamical behaviour. We analyze this model using tools from Petri-net theory and study its dynamical
range that is consistent with currently available knowledge by conditioning model parameters on the available
data in an approximate Bayesian computation (ABC) framework. Within this ABC approach we analyze stochastic
and deterministic dynamics. This analysis allows us to identify different types of behaviour and these mechanistic
insights can in turn be used to design new, more detailed and time-resolved experiments.
Conclusions: We have developed the first mechanistic model of the Psp response in E. coli. This model allows us
to predict the possible qualitative stochastic and deterministic dynamic behaviours of key molecular players in the
stress response. Our inferential approach can be applied to stress response and signalling systems more generally:
in the ABC framework we can condition mathematical models on qualitative data in order to delimit e.g.
parameter ranges or the qualitative system dynamics in light of available end-point or qualitative information.
Date Issued
2011-05-12
Date Acceptance
2011-05-12
Citation
BMC Systems Biology, 2011, 5
ISSN
1752-0509
Publisher
BioMed Central
Journal / Book Title
BMC Systems Biology
Volume
5
Copyright Statement
© 2011 Toni et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Article Number
69