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