=Paper=
{{Paper
|id=None
|storemode=property
|title=Modelling Atopic Dermatitis using Petri Nets
|pdfUrl=https://ceur-ws.org/Vol-852/paper6.pdf
|volume=Vol-852
}}
==Modelling Atopic Dermatitis using Petri Nets==
https://ceur-ws.org/Vol-852/paper6.pdf
Proc. BioPPN 2012, a satellite event of PETRI NET 2012
Modelling Atopic Dermatitis using Petri Nets
Marta E Polak
Clinical and Experimental Sciences, University of Southampton, Faculty of Medicine,
Mailpoint 825, Level F, South Block, Sir Henry Wellcome Laboratories, Southampton
General Hospital, Southampton, SO16 6YD, United Kingdom,
m.e.polak@soton.ac.uk
1 Background
Atopic dermatitis (AD) is a disorder of inflammation in the skin and is strongly
associated with other inflammatory epithelial atopic conditions (asthma, allergic
rhinitis and food allergy). The pathogenesis of AD results from complex interac-
tions between susceptibility genes encoding skin barrier molecules and markers
of the inflammatory response, host environments, infectious agents, and spe-
cific immunologic responses [1-3]. Despite the long-held knowledge about the
multiple immunological processes involved in AD pathogenesis (including role of
dendritic cells, kerationocytes and T lymphocytes, interactions between different
cell types, effect of allergen exposure, trauma and infection with Staphylococ-
cus Aureus on the AD exacerbations) the precise reason for the bias towards
a cutaneous Th2 response in AE still remains obscure. It seems likely that un-
derstanding of the complex cross-talk between different components of the cu-
taneous immune network is fundamental to understanding of the Th2 polarised
inflammation in AD.
To gain in-depth understanding and be able to predict the behavior of the
system, we sought to create an integrated in silico model compositionally from
relatively simple individual components that are amenable to detailed mathe-
matical analysis. The simple, initial model described in this contribution will
serve as a high level architectural specification of AD skin and immune system
interactions. In the future we plan to harness data from microarray analysis in
order to refine the individual model components by incorporating the recon-
structed signal transduction pathways within cellular components.
2 Model description
Petri nets have been widely used in modelling biochemical, genetic, signaling and
metabolic networks [7-9]. We however feel that the modelling of AD requires
a multistep approach, embedding the investigations of the internal genetic or
metabolic signaling cascades within the detailed analysis of the interactions be-
tween the cellular components of the cutaneous immune system. The preliminary
model of cell-to-cell signaling in AD exposed to allergen and infected with S.A.
incorporates three cell types (KC, DC and T cells), where KC and T cells are
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Proc. BioPPN 2012, a satellite event of PETRI NET 2012
simplistically represented as places. The signaling within DC is represented by
two independent pathways, TNF-α–NFκB-dependent IL-12p70 initiating Th1
responses and TSLP-STAT3-OX-40L pathway inducing Th2 responses. The sig-
naling proteins are represented as places and are connected by activating (black
arrows) and inhibiting (red diamond arrows) edges.
The model was constructed using yEd graphical software, and BioLayout
Express3D [10] was used to simulate the signal flow allowing investigating the
dynamic behaviour of the network dependently on presence/absence of inhibition
edges, conservation or consumption of the tokens at the transitions, and the value
of initial stimulation given by the number of tokens. To represent healthy skin
and AD skin firings via TNF-α or TSLP were inhibited, respectively, and the
simulation for altered number of tokens at each starting place was repeated as
for the initial state of the network. Similarly, the effect of S.A. infection (0-1000
tokens) and combined effect of Der-p-1 and S.A. in exclusive Th1/Th2 or both
pathways enabled models was investigated.
As assumed, stimulation with der-p-1 in AD model induced only Th2 re-
sponses, which were greatly enhanced in the presence of Staphylococcal infec-
tion. While in normal skin model infection with SA led to induction of both Th1
and Th2 responses, stimulation of AD model resulted in Th2 skewing. Induc-
tion of Th2 polarisation depended on blocking of the Th1 signaling, rather than
enhancing Th2 signaling.
Acknowledgements
I am very grateful to Dr. Mike Ardern-Jones and Dr. Pawel Sobocinski for critical
review of the manuscript. I would also like to thank Prof. Tom Freeman for advice
on Petri nets and model construction. This study has been funded by British
Skin Foundation.
References
1. Boguniewicz M, Leung DY. Atopic dermatitis: a disease of altered skin barrier and
immune dysregulation. Immunol Rev. 242(1):233-46.( 2011 )
2. Irvine AD, McLean WH, Leung DY. Filaggrin mutations associated with skin and
allergic diseases. N Engl J Med. 365(14):1315-27 (2011)
3. Hamid Q, Boguniewicz M, Leung DY. Differential in situ cytokine gene expression
in acute versus chronic atopic dermatitis. J Clin Invest. 1994 Aug;94(2):870-6.
4. Niebuhr M, Werfel T. Innate immunity, allergy and atopic dermatitis. Curr Opin
Allergy Clin Immunol. 10(5):463-8. (2010)
5. Zaba LC, Krueger JG, Lowes MA. Resident and ”inflammatory” dendritic cells in
human skin. J Invest Dermatol. 129(2):302-8 (2009)
6. Soumelis V, Reche PA, Kanzler H, Yuan W, Edward G, Homey B, Gilliet M, Ho
S,Antonenko S, Lauerma A, Smith K, Gorman D, Zurawski S, Abrams J, Menon
S,McClanahan T, de Waal-Malefyt Rd R, Bazan F, Kastelein RA, Liu YJ. Human
epithelial cells trigger dendritic cell mediated allergic in-flammation by producing
TSLP. Nat Immunol. 3(7):673-80 (2002)
- 45 -
Proc. BioPPN 2012, a satellite event of PETRI NET 2012
7. Ruths D, Muller M, Tseng JT, Nakhleh L, Ram PT. The signaling petri net-based
simulator: a non-parametric strategy for characterizing the dynamics of cell-specific
signaling networks. PLoS Comput Biol. 29;4(2):e1000005 (2008)
8. Heiner M, Koch I, Will J. Model validation of biological pathways using Petri
nets–demonstrated for apoptosis. Biosystems. 75(1-3):15-28 (2004)
9. Moore JH, Boczko EM, Summar ML. Connecting the dots between
genes,biochemistry, and disease susceptibility: systems biology modeling in hu-man
genetics. Mol Genet Metab. 84(2):104-11. (2005)
10. Theocharidis A, van Dongen S, Enright AJ, Freeman TC. Network visualization
and analysis of gene expression data using BioLayout Express(3D). Nat Protoc.
4(10):1535-50 (2009)
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