source: liacs/pnbm/project/report.tex@ 12

Last change on this file since 12 was 12, checked in by Rick van der Zwet, 15 years ago

For peer-review

File size: 5.3 KB
Line 
1%
2% $Id: report.tex 571 2008-04-20 17:31:04Z rick $
3%
4
5\documentclass[12pt,a4paper]{article}
6
7\frenchspacing
8\usepackage[english]{babel}
9\selectlanguage{english}
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}
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.
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
59the creation of a new property (figure: $2:1$), with the following properties:
60
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
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
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}
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
92developmental) biology.
93
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
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
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$B$.
108
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
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}
Note: See TracBrowser for help on using the repository browser.