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  • /liacs/README

    r30 r20  
    33alg             =   Algorimiek
    44bedrijfsethiek  =   Bedrijfsethiek
    5 pnbm            =   PetriNets and BioModeling (master)
     5bpn             =   Biomodeling and PetriNets (master)
    66ca              =   Computer Architectuur
    77ccs             =   Challenges in Computer Science
    … …  
    1616nc              =   Natural Computing
    1717net             =   Netwerken
    18 mms             =   MultiMediaSystems
    1918os              =   Operating Systems
    2019penc            =   Programmeren en Correctheid
    21 pnbm            =   PetriNets and BioModeling
    2220pm              =   ProgrammeerMethoden
    2321re              =   Requirements Engineering

  • /liacs/nc/low-correlation/Makefile

    r30 r20  
    11BIN=main
    2 CFLAGS=-lm
     2CFLAGS="-fnested-functions"
    33
    4 mcs.out: mcs.m
    5         @octave -q mcs.m
     4all: $(BIN)
     5
     6run: $(BIN)
     7        ./$(BIN)

  • /liacs/pnbm/project/latex.mk

    r30 r20  
    1010DVIPDF = dvipdf
    1111ASPELL = aspell
    12 ASPELL_ARGS = -c --mode=tex -x
     12ASPELL_ARGS = -c --mode=tex
    1313
    1414#XXX: Make me dynamic

  • /liacs/pnbm/project/report.tex

    r30 r20  
    1414\usepackage{amssymb,amsmath}
    1515
    16 \title{Modeling planar signalling in AP axis development in \emph{Xenopus laevis}\\
     16\title{DRAFT: 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 within the development of the AP axis
    27 development of the \emph{Xenopus laevis} \cite{Bertens09} in which cells
    28 accumulate proteins based on the saturation of nearby cells. If one cell
    29 produces n amount of proteins, it will initiate a transferring cascade to cells
    30 in the vicinity.  This dissemination of proteins will eventually cease,
    31 considering that n is a finite variable. There is a gradation in the amount of
    32 proteins transferred, meaning that neighbouring cells get n/2 the amount of
    33 proteins of the most saturated cell.
     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.
    3432
    35 We are going to model this into Petri-Nets beeing a mathematical modeling
    36 language, which suit well for this purpose as we could nicely model the process
    37 in graphical interactive representation and could also be used for automated
    38 model tracking and analyze.
     33XXX: Citing to the Bio Papers
     34XXX: Small introductions Petri-nets
    3935
    40 \section{Approach}
    41 First a Petri-Net model will be defined textually and using graphs next the
    42 modeling will be taking into practice using the modeling tool\emph{CPNTools}
    43 \footnote{http://wiki.daimi.au.dk/cpntools/cpntools.wiki}.
     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}.
    4439
    4540\section{Modeling}
    46 To model this process we will take a modular approach using coloured Petri-Nets
     41To model this process we will take a modular approach using coloured PetriNets
    4742(see Fig~\ref{fig:model}), since the goal of this assignment is to have a
    4843solution that can be applied to any configuration of cells. We start with a
    49 building block that is an abstraction of a cell (figure: circle), which can then
     44bulding block that is an abstraction of a cell (figure: circle), which can then
    5045be coupled to other cells (figure: arrows). The abstraction contains two
    5146different types. First the proteins are modelled (figure: red), secondly the
    5247proteins (figure: blue) are leading in a second process of the creation of
    53 posterisation which also needs modeling. We assume a 1:1 mapping between the amount
    54 of proteins and the posterisation -this taken into consideration- ones an
    55 \texttt{INITIAL} protein is 'used' (e.g. has on posterisation counterpart) in
    56 this process it
     48gradients which also needs modeling. We assume a 1:1 mapping between the amount
     49of proteins and gradients -this taken into consideration- ones an \texttt{INITIAL}
     50protein is 'used' (e.g. has on posterisation counterpart) in this process it
    5751get called \texttt{ACTIVATED}. We assume that the proteins to posterisation
    5852process is taking place at the same time as the proteins distribution. And in a
    … …  
    6862posterisation present.
    6963
    70 The connectors between the cells (the membranes) has a special properly. One
    71 can see them as pressure valves others as siphons (see Fig~\ref{fig:pressure}).
     64The connectors between the cells (the membrams) has a special properly. One
     65can see them as pressure valves others as sighons (see Fig~\ref{fig:pressure}).
    7266The moment the 'volume' at complies with the following properly $A / 2 < B$
    7367then the pressure closes, else it passes volume from A to B at an certain rate
    7468(\texttt{flowSpeed}). This rate could depend on the difference, actual value present
    75 or something else. Please do mind that negative values could ever appear hence
    76 the checking whether the source is bigger or equal then the flowSpeed.
     69or something else.
    7770
    78 For the case there exists no standard Petri-Net 'component', hence  this require
     71For the case there exists no standard PetriNet 'component', hence  this require
    7972the creation of a new property (figure: $2:1$), with the following properties:
    8073
    8174\begin{verbatim}
    8275flowSpeed = n
    83 if A > 2 * B and A => flowSpeed then
     76if A > 2 * B then
    8477  A = A - flowSpeed
    8578  B = B + flowSpeed
    86 else if B > 2 * A and B => flowSpeed then
     79else if B > 2 * A then
    8780  B = B - flowSpeed
    8881  A = A + flowSpeed
    … …  
    9184
    9285Planar signaling could theoretically start in every cell, by
    93 inserting some amount of proteins. In our model represented as a bunch of
    94 \texttt{INITIAL} tokens being put in a random cell.
     86inserting some amount of protiens. In our model represented as a bunch of
     87\texttt{INITIAL} tokens beeing put in a random cell.
    9588
    9689\begin{figure}[htp]
    … …  
    113106
    114107One it the shortcoming of the 'balancing'. It does not allow reading of how
    115 many tokens are present in a certain state and base action upon them. As
     108many tokens are present in a certain state and base action uppon them. As
    116109workaround for this (see Fig~\ref{fig:CPNplanar}) we used a 'dump' gradation
    117110function. In our case it simply take 3 tokens and pushes 1 forward and
    … …  
    124117at the head or the tail.
    125118
    126 In this implementation the proteins to gradients  process is taking place at
    127 cell $A$ at the same time that the proteins get transferred from cell $A$ to
     119In this implementation the protiens to gradiants  process is taking place at
     120cell $A$ at the same time that the proteins get transfered from cell $A$ to
    128121$B$.
    129122
    130123Also it should be noted that it missing a notion of timed firing sequences;
    131124meaning firing sequences which will occur at an certain time. This could for
    132 example used to 'trigger' a timed activation of the \texttt{INITIAL} to
     125example used to 'trigger' a timmed activation of the \texttt{INITIAL} to
    133126\texttt{ACTIVATED} process as modeled in fig~\ref{fig:model}. An initial idea
    134 is shown at fig~\ref{fig:time-idea} in appendix~\ref{sec:timer-idea}.
     127is shown at fig\ref{fig:time-idea} in appendix 1.
    135128
    136129
    … …  
    145138
    146139\section{Conclusion}
    147 Using Petri-Nets for modeling biology processes is a powerful framework, which
    148 could be well expandable. The Proof Of Concept implementations and
     140Using PetriNets for modeling biology processes is a powerful framework, which
     141could be well extendable. The Proof Of Concept implementations and
    149142visualisations how-ever are lacking. \emph{CPNTools} for example does not
    150 provide a powerful enough tool-set for the modeling purposes.
     143provide a powerfull enough toolset for the modeling purposes.
    151144
    152 \bibliographystyle{amsalpha}
    153145\begin{thebibliography}{10}
    154 \bibitem[Bertens09]{Bertens09}Laura M.F. Bertens et al., Using Petri Nets in Higher
    155 Level Developmental Biology: A case study on the AP axis development in Xenopus
    156 laevis Extended Abstract, 2009
     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
    157156\end{thebibliography}
    158 \appendix
    159 \section{Timer Idea}
    160 \label{sec:timer-idea}
     157\section{*Appendix}
    161158
    162159\begin{figure}[htp]

  • /liacs/templates/latex.mk

    r30 r20  
    1111ASPELL = aspell
    1212ASPELL_ARGS = -c --mode=tex
    13 
    14 #XXX: Make me dynamic
    15 VIEWPDF = evince
    1613.SUFFIXES: .pdf
    1714
    … …  
    3734
    3835viewpdf: buildpdf
    39         $(VIEWPDF) $(TARGET).pdf
     36        xpdf $(TARGET).pdf
    4037
    4138spell: spell-en
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