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2016 ; 10
(1
): 38
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A rule-based model of insulin signalling pathway
#MMPMID27245161
Di Camillo B
; Carlon A
; Eduati F
; Toffolo GM
BMC Syst Biol
2016[Jun]; 10
(1
): 38
PMID27245161
show ga
BACKGROUND: The insulin signalling pathway (ISP) is an important biochemical
pathway, which regulates some fundamental biological functions such as glucose
and lipid metabolism, protein synthesis, cell proliferation, cell differentiation
and apoptosis. In the last years, different mathematical models based on ordinary
differential equations have been proposed in the literature to describe specific
features of the ISP, thus providing a description of the behaviour of the system
and its emerging properties. However, protein-protein interactions potentially
generate a multiplicity of distinct chemical species, an issue referred to as
"combinatorial complexity", which results in defining a high number of state
variables equal to the number of possible protein modifications. This often leads
to complex, error prone and difficult to handle model definitions. RESULTS: In
this work, we present a comprehensive model of the ISP, which integrates three
models previously available in the literature by using the rule-based modelling
(RBM) approach. RBM allows for a simple description of a number of signalling
pathway characteristics, such as the phosphorylation of signalling proteins at
multiple sites with different effects, the simultaneous interaction of many
molecules of the signalling pathways with several binding partners, and the
information about subcellular localization where reactions take place. Thanks to
its modularity, it also allows an easy integration of different pathways. After
RBM specification, we simulated the dynamic behaviour of the ISP model and
validated it using experimental data. We the examined the predicted profiles of
all the active species and clustered them in four clusters according to their
dynamic behaviour. Finally, we used parametric sensitivity analysis to show the
role of negative feedback loops in controlling the robustness of the system.
CONCLUSIONS: The presented ISP model is a powerful tool for data simulation and
can be used in combination with experimental approaches to guide the experimental
design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to
Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODEL 1604100005).
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