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  • /liacs/README

    r20 r30  
    33alg             =   Algorimiek
    44bedrijfsethiek  =   Bedrijfsethiek
    5 bpn             =   Biomodeling and PetriNets (master)
     5pnbm            =   PetriNets and BioModeling (master)
    66ca              =   Computer Architectuur
    77ccs             =   Challenges in Computer Science
    … …  
    1616nc              =   Natural Computing
    1717net             =   Netwerken
     18mms             =   MultiMediaSystems
    1819os              =   Operating Systems
    1920penc            =   Programmeren en Correctheid
     21pnbm            =   PetriNets and BioModeling
    2022pm              =   ProgrammeerMethoden
    2123re              =   Requirements Engineering

  • /liacs/nc/low-correlation/Makefile

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    11BIN=main
    2 CFLAGS="-fnested-functions"
     2CFLAGS=-lm
    33
    4 all: $(BIN)
    5 
    6 run: $(BIN)
    7         ./$(BIN)
     4mcs.out: mcs.m
     5        @octave -q mcs.m

  • /liacs/pnbm/project/latex.mk

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    1010DVIPDF = dvipdf
    1111ASPELL = aspell
    12 ASPELL_ARGS = -c --mode=tex
     12ASPELL_ARGS = -c --mode=tex -x
    1313
    1414#XXX: Make me dynamic

  • /liacs/pnbm/project/report.tex

    r20 r30  
    1414\usepackage{amssymb,amsmath}
    1515
    16 \title{DRAFT: Modeling planar signalling in AP axis development in \emph{Xenopus laevis}\\
     16\title{Modeling planar signalling in AP axis development in \emph{Xenopus laevis}\\
    1717\large{using Petri Nets in Higher Level Developmental Biology}}
    1818\author{Rick van der Zwet, Tiago Borges Coelho \\
    … …  
    2424\maketitle
    2525\section{Abstract}
    26 Planar signaling is the process in which cells accumulate proteins based on the
    27 saturation of nearby cells. If one cell produces n ammount of proteins, it will
    28 initiate a transfering cascade to cells in the vicinity. This dissemination of
    29 proteins will eventually cease, considering that n is a finite variable. There
    30 is a gradation in the ammount of proteins transfered, meaning that neighbouring
    31 cells get n/2 the ammount of proteins of the most saturated cell.
     26Planar signaling is the process within the development of the AP axis
     27development of the \emph{Xenopus laevis} \cite{Bertens09} in which cells
     28accumulate proteins based on the saturation of nearby cells. If one cell
     29produces n amount of proteins, it will initiate a transferring cascade to cells
     30in the vicinity.  This dissemination of proteins will eventually cease,
     31considering that n is a finite variable. There is a gradation in the amount of
     32proteins transferred, meaning that neighbouring cells get n/2 the amount of
     33proteins of the most saturated cell.
    3234
    33 XXX: Citing to the Bio Papers
    34 XXX: Small introductions Petri-nets
     35We are going to model this into Petri-Nets beeing a mathematical modeling
     36language, which suit well for this purpose as we could nicely model the process
     37in graphical interactive representation and could also be used for automated
     38model tracking and analyze.
    3539
    36 \section{Approch}
    37 First a PetriNet model will be defined textually and using graphs next the
    38 modeling will be taking into practice using the modeling tool\emph{CPNTools}.
     40\section{Approach}
     41First a Petri-Net model will be defined textually and using graphs next the
     42modeling will be taking into practice using the modeling tool\emph{CPNTools}
     43\footnote{http://wiki.daimi.au.dk/cpntools/cpntools.wiki}.
    3944
    4045\section{Modeling}
    41 To model this process we will take a modular approach using coloured PetriNets
     46To model this process we will take a modular approach using coloured Petri-Nets
    4247(see Fig~\ref{fig:model}), since the goal of this assignment is to have a
    4348solution that can be applied to any configuration of cells. We start with a
    44 bulding block that is an abstraction of a cell (figure: circle), which can then
     49building block that is an abstraction of a cell (figure: circle), which can then
    4550be coupled to other cells (figure: arrows). The abstraction contains two
    4651different types. First the proteins are modelled (figure: red), secondly the
    4752proteins (figure: blue) are leading in a second process of the creation of
    48 gradients which also needs modeling. We assume a 1:1 mapping between the amount
    49 of proteins and gradients -this taken into consideration- ones an \texttt{INITIAL}
    50 protein is 'used' (e.g. has on posterisation counterpart) in this process it
     53posterisation which also needs modeling. We assume a 1:1 mapping between the amount
     54of proteins and the posterisation -this taken into consideration- ones an
     55\texttt{INITIAL} protein is 'used' (e.g. has on posterisation counterpart) in
     56this process it
    5157get called \texttt{ACTIVATED}. We assume that the proteins to posterisation
    5258process is taking place at the same time as the proteins distribution. And in a
    … …  
    6268posterisation present.
    6369
    64 The connectors between the cells (the membrams) has a special properly. One
    65 can see them as pressure valves others as sighons (see Fig~\ref{fig:pressure}).
     70The connectors between the cells (the membranes) has a special properly. One
     71can see them as pressure valves others as siphons (see Fig~\ref{fig:pressure}).
    6672The moment the 'volume' at complies with the following properly $A / 2 < B$
    6773then the pressure closes, else it passes volume from A to B at an certain rate
    6874(\texttt{flowSpeed}). This rate could depend on the difference, actual value present
    69 or something else.
     75or something else. Please do mind that negative values could ever appear hence
     76the checking whether the source is bigger or equal then the flowSpeed.
    7077
    71 For the case there exists no standard PetriNet 'component', hence  this require
     78For the case there exists no standard Petri-Net 'component', hence  this require
    7279the creation of a new property (figure: $2:1$), with the following properties:
    7380
    7481\begin{verbatim}
    7582flowSpeed = n
    76 if A > 2 * B then
     83if A > 2 * B and A => flowSpeed then
    7784  A = A - flowSpeed
    7885  B = B + flowSpeed
    79 else if B > 2 * A then
     86else if B > 2 * A and B => flowSpeed then
    8087  B = B - flowSpeed
    8188  A = A + flowSpeed
    … …  
    8491
    8592Planar signaling could theoretically start in every cell, by
    86 inserting some amount of protiens. In our model represented as a bunch of
    87 \texttt{INITIAL} tokens beeing put in a random cell.
     93inserting some amount of proteins. In our model represented as a bunch of
     94\texttt{INITIAL} tokens being put in a random cell.
    8895
    8996\begin{figure}[htp]
    … …  
    106113
    107114One it the shortcoming of the 'balancing'. It does not allow reading of how
    108 many tokens are present in a certain state and base action uppon them. As
     115many tokens are present in a certain state and base action upon them. As
    109116workaround for this (see Fig~\ref{fig:CPNplanar}) we used a 'dump' gradation
    110117function. In our case it simply take 3 tokens and pushes 1 forward and
    … …  
    117124at the head or the tail.
    118125
    119 In this implementation the protiens to gradiants  process is taking place at
    120 cell $A$ at the same time that the proteins get transfered from cell $A$ to
     126In this implementation the proteins to gradients  process is taking place at
     127cell $A$ at the same time that the proteins get transferred from cell $A$ to
    121128$B$.
    122129
    123130Also it should be noted that it missing a notion of timed firing sequences;
    124131meaning firing sequences which will occur at an certain time. This could for
    125 example used to 'trigger' a timmed activation of the \texttt{INITIAL} to
     132example used to 'trigger' a timed activation of the \texttt{INITIAL} to
    126133\texttt{ACTIVATED} process as modeled in fig~\ref{fig:model}. An initial idea
    127 is shown at fig\ref{fig:time-idea} in appendix 1.
     134is shown at fig~\ref{fig:time-idea} in appendix~\ref{sec:timer-idea}.
    128135
    129136
    … …  
    138145
    139146\section{Conclusion}
    140 Using PetriNets for modeling biology processes is a powerful framework, which
    141 could be well extendable. The Proof Of Concept implementations and
     147Using Petri-Nets for modeling biology processes is a powerful framework, which
     148could be well expandable. The Proof Of Concept implementations and
    142149visualisations how-ever are lacking. \emph{CPNTools} for example does not
    143 provide a powerfull enough toolset for the modeling purposes.
     150provide a powerful enough tool-set for the modeling purposes.
    144151
     152\bibliographystyle{amsalpha}
    145153\begin{thebibliography}{10}
    146 %   sing Petri Nets in Higher Level Developmental Biology:
    147 % A case study on the AP axis development in Xenopus laevis
    148 %                    Extended Abstract
    149 % http://www.liacs.nl/~csbpn/COURSE%20DOCUMENTS/extended%20abstract%20Bertens%20Jansen%20Kleijn%20Koutny%20Verbeek.pdf
    150 % Laura M.F. Bertens
    151 
    152 % http://www.liacs.nl/~csbpn/
    153 %
    154 %
    155 
     154\bibitem[Bertens09]{Bertens09}Laura M.F. Bertens et al., Using Petri Nets in Higher
     155Level Developmental Biology: A case study on the AP axis development in Xenopus
     156laevis Extended Abstract, 2009
    156157\end{thebibliography}
    157 \section{*Appendix}
     158\appendix
     159\section{Timer Idea}
     160\label{sec:timer-idea}
    158161
    159162\begin{figure}[htp]

  • /liacs/templates/latex.mk

    r20 r30  
    1111ASPELL = aspell
    1212ASPELL_ARGS = -c --mode=tex
     13
     14#XXX: Make me dynamic
     15VIEWPDF = evince
    1316.SUFFIXES: .pdf
    1417
    … …  
    3437
    3538viewpdf: buildpdf
    36         xpdf $(TARGET).pdf
     39        $(VIEWPDF) $(TARGET).pdf
    3740
    3841spell: spell-en
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