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[11]1%
2% $Id: report.tex 571 2008-04-20 17:31:04Z rick $
3%
4
5\documentclass[12pt,a4paper]{article}
6
7\frenchspacing
[12]8\usepackage[english]{babel}
9\selectlanguage{english}
[11]10\usepackage{graphicx}
11\usepackage{url}
12\usepackage{multicol}
13\usepackage{fancybox}
14\usepackage{amssymb,amsmath}
15
16\title{DRAFT: Modeling planar signalling in AP axis development in \emph{Xenopus laevis}\\
17\large{using Petri Nets in Higher Level Developmental Biology}}
18\author{Rick van der Zwet, Tiago Borges Coelho \\
19 \texttt{<hvdzwet@liacs.nl>,<borges.coelho@gmail.com>}\\
20 LIACS - Leiden University, The Netherlands}
21\date{\today}
22
23\begin{document}
24\maketitle
25\section{Abstract}
26Planar signaling is the process in which cells accumulate proteins based on the
27saturation of nearby cells. If one cell produces n ammount of proteins, it will
28initiate a transfering cascade to cells in the vicinity. This dissemination of
29proteins will eventually cease, considering that n is a finite variable. There
30is a gradation in the ammount of proteins transfered, meaning that neighbouring
31cells get n/2 the ammount of proteins of the most saturated cell.
32
33XXX: Citing to the Bio Papers
34XXX: Small introductions Petri-nets
35
36\section{Approch}
37First a PetriNet model will be defined textually and using graphs next the
38modeling will be taking into practice using the modeling tool\emph{CPNTools}.
39
40\section{Modeling}
[12]41To model this process we will take a modular approach using coloured PetriNets
42(see Fig~\ref{fig:model}), since the goal of this assignment is to have a
43solution that can be applied to any configuration of cells. We start with a
44bulding block that is an abstraction of a cell (figure: circle), which can then
45be coupled to other cells (figure: arrows). The abstraction contains two
46different types. First the proteins are modelled (figure: red), secondly the
47proteins (figure: blue) are leading in a second process of the creation of
48gradients which also needs modeling. We assume a 1:1 mapping between the amount
49of proteins and gradients -this taken into consideration- ones an $INITIAL$
50protein is used in this process it get called $ACTIVATED$. We assume that the
51proteins to gradients process is starting at an certain time from the start of
52the process.
[11]53
54The connectors between the cells (the membrams) has a special properly. One
55can see them as pressure valves others as sighons (see Fig~\ref{fig:pressure}).
56The moment the 'volume' at complies with the following properly $A / 2 < B$
57then the pressure closes, else it passes volume from A to B at an certain rate.
58For the case there exists no standard PetriNet 'component', hence this require
[12]59the creation of a new property (figure: $2:1$), with the following properties:
[11]60
[12]61\begin{verbatim}
62flowSpeed = 2
63if A > 2 * B then
64 A = A - flowSpeed
65 B = B + flowSpeed
66else if B > 2 * A then
67 B = B - flowSpeed
68 A = A + flowSpeed
69endif
70\end{verbatim}
71
[11]72Planar signaling could theoretically start in every cell, by
73inserting some amount of protiens. In our model represented as a bunch of
74$INITIAL$ tokens beeing put in a random cell.
75
[12]76\begin{figure}[htp]
77\centering
78\caption{Planar signaling model}
79\includegraphics[width=60mm]{planar-signaling-model.eps}
80\label{fig:model}
81\end{figure}
[11]82
83\begin{figure}[htp]
84\centering
85\caption{Pressure valve example}
86\includegraphics[height=60mm]{pressure-valve.eps}
87\label{fig:pressure}
88\end{figure}
89
90\section{CPNTools 'implementation'}
91CPNTools has quite some shortcomings when it comes to modeling (higher level
[12]92developmental) biology.
[11]93
[12]94One it the shortcoming of the 'balancing'. It does not allow reading of how
95many tokens are present in a certain state and base action uppon them. As
96workaround for this (see Fig~\ref{fig:CPNplanar}) we used a 'dump' gradation
97function. In our case it simply take 3 tokens and pushes 1 forward and
98converting 2 directly to $ACTIVATED$. This does not take in consideration if
99the amount get changed in 'further-up', by some external source.
100
[11]101Secondly it is missing a possibility to for easy random initialisation for
102modeling purposes. As a dirty quirk we 'hacked' it to choose between starting
103at the head or the tail.
104
[12]105In this implementation the protiens to gradiants process is taking place at
106cell $A$ at the same time that the proteins get transfered from cell $A$ to
107$B$.
[11]108
[12]109Also it should be noted that it missing a notion of timed firing sequences;
110meaning firing sequences which will occur at an certain time. This could for
111example used to 'trigger' a timmed activation of the $INITIAL$ to $ACTIVATED$
112process as modeled in fig~\ref{fig:model}.
113
[11]114\begin{figure}[htp]
115\centering
116\caption{CPNTools implementation}
117\advance\leftskip-2cm
118\advance\rightskip+2cm
119\includegraphics[width=1.3\textwidth]{planer-signaling.eps}
120\label{fig:CPNplanar}
121\end{figure}
122
123\section{Conclusion}
124Using PetriNets for modeling biology processes is a powerful framework, which
125could be well extendable. The Proof Of Concept implementations and
126visualisations how-ever are lacking. \emph{CPNTools} for example does not
127provide a powerfull enough toolset for the modeling purposes.
128
129\begin{thebibliography}{10}
130% sing Petri Nets in Higher Level Developmental Biology:
131% A case study on the AP axis development in Xenopus laevis
132% Extended Abstract
133% http://www.liacs.nl/~csbpn/COURSE%20DOCUMENTS/extended%20abstract%20Bertens%20Jansen%20Kleijn%20Koutny%20Verbeek.pdf
134% Laura M.F. Bertens
135
136% http://www.liacs.nl/~csbpn/
137%
138%
139
140\end{thebibliography}
141\end{document}
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