Cell Illustrator™: User Guide© 2002-2017 Human Genome Center, Institute of Medical Science,
The University of Tokyo
All rights reserved.

TABLE OF CONTENTS:
1

INTRODUCTION ................................................................................................. 4

2 GETTING STARTED .......................................................................................... 5
2.1
Cell Illustrator Desktop Installation ............................................................... 5
2.2
Cell Illustrator Desktop Startup ..................................................................... 5
2.3
Licensing Information ..................................................................................... 5
2.4
CI Memory Customization.............................................................................. 5
2.5
Uninstallation.................................................................................................... 6
3 CELL ILLUSTRATOR ELEMENTS ................................................................. 7
3.1
Concept Overview ........................................................................................... 7
3.2
Entities .............................................................................................................. 8
3.3
Connectors ....................................................................................................... 9
3.4
Processes ....................................................................................................... 10
3.5
Element Combinations ................................................................................. 11
3.6
Discrete Elements Example 1 ..................................................................... 12
3.7
Discrete Elements Example 2: Connector Speed .................................... 13
3.8
Continuous Elements Example 1................................................................ 14
3.9
Continuous Elements Example 2................................................................ 16
4 BIOLOGICAL PROCESS MODELING WITH CELL ILLUSTRATOR ..... 18
4.1
Degradation .................................................................................................... 18
4.2
Translocation.................................................................................................. 20
4.3
Transcription .................................................................................................. 21
4.4
Complex .......................................................................................................... 23
4.5
Tetramerization .............................................................................................. 25
4.6
Stochastic Tetramerization .......................................................................... 26
4.7
Custom Tetramerization ............................................................................... 27
4.8
Separation ...................................................................................................... 28
4.9
Inhibition.......................................................................................................... 30
4.10 Catalysis ......................................................................................................... 32
5 CELL ILLUSTRATOR USER INTERFACE AND MODEL CREATION .. 35
5.1
Add Elements................................................................................................. 35
5.2
Modify a Model .............................................................................................. 36
5.3
Change Element Properties ........................................................................ 37
5.4
Element Settings Frame and Element Lists Frame ................................. 38
5.5
Biological Elements and Pathway Fragments .......................................... 45
5.6
Script Editor .................................................................................................... 50
5.7
Preferences Frame ....................................................................................... 53
5.8
Graph Layout Frame..................................................................................... 54
6

SIMULATION ..................................................................................................... 55© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 2 of 85

6.1
6.2
6.3
6.4

Simulation Concept ....................................................................................... 55
How to Set Up and Run Simulation ............................................................ 57
Viewing and Analyzing Simulation Results ............................................... 62
Running SECG Simulation .......................................................................... 63

7 TOOLS FOR MODEL ANALYSIS ................................................................. 67
7.1
Navigator ........................................................................................................ 67
7.2
Finder .............................................................................................................. 68
7.3
Print and Print Area....................................................................................... 69
7.4
Export Options ............................................................................................... 69
8 MAKING COMPLEX MODELS ...................................................................... 70
8.1Grouping and Ungrouping............................................................................ 70
9CELL ILLUSTRATOR PLAYER .................................................................... 72

APPENDIX A: TUTORIAL—MODELING FAS LIGAND INDUCEDAPOPTOSIS .............................................................................................................. 74
APPENDIX B: USING DIFFERENTIAL EQUATIONS IN CI ............................. 81
APPENDIX C: KINETIC STYLES .......................................................................... 83
CI REFERENCES ..................................................................................................... 85© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 3 of 85

1 Introduction
Cell Illustrator™ (CI) is a software tool that enables biologists to model,
elucidate and simulate complex biological processes and systems [11]. It allows
researchers to model metabolic pathways, signal transduction cascades, gene
regulatory pathways as well as dynamic interactions of various biological entities
such as genomic DNA, mRNA and proteins. Cell Illustrator models are used tovisualize biopathways, interpret experimental data and test hypotheses. In addition, it
provides researchers with model diagrams of publication quality and simulation result
charts. Cell Illustrator has been successfully utilized to model biological pathways
like Circadian Rhythms of Drosophila, Glycolytic pathway and Fas ligand induced
Apoptosis [1, 2, 3, 4, 5, 6, and 7].
Using the graphical user interface of Cell Illustrator, researchers can createand simulate their own molecular pathway models. Some of the key features of Cell
Illustrator are: (a) Pathway construction and visualization—construct models
graphically by drag and drop entities that represent bio-components in the biologicalpathways; (b) Pathways simulation—specify mathematical formulas for biochemical
reactions in the pathway for simulation; The simulation can be run inside theworkspace window in an interactive mode (c) Simulation replay – the simulation
results can be logged and then visualized and analyzed in Cell Illustrator Player;
The documentation for the Cell Illustrator software includes the manuals listed
below:
Cell Illustrator User Manual: Introduction to Cell Illustrator system
Cell Illustrator Reference Manual: Detailed description of Cell Illustrator
functionalities
Cell Illustrator Player Reference Manual: Detailed description of Cell
Illustrator Player functionalities

This manual describes version 5.0 of Cell Illustrator Desktop (CI5.0).

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 4 of 85

2 Getting Started
2.1

Cell Illustrator Desktop Installation

The Cell Illustrator installer is a Java application and requires Java Runtime
Environment. Before starting the installation make sure you have Java installed onyour PC. If you do not have Java on your PC, go to http://java.com and install it.
To install CI please do the following steps:

2.2-

Download the installer package ci-setup.jar-

Execute the installer package by double clicking on ci-setup.jar or
by executing the command java –jar ci-setup.jar-

Follow the instructions on the screen

Cell Illustrator Desktop Startup

Cell Illustrator is a Java desktop application. After completing the CI
installation, short-cuts to the Cell Illustrator startup script should appear on the
Desktop folder, in the Start Menu and/or in the Applications folder.
To start Cell Illustrator, please do one of the following:

2.3-

Locate Cell Illustrator short-cut in one of the above mentioned locations
and execute it-

Locate the Cell Illustrator startup script, ci50.bat or ci50.sh in the
installation folder and execute it.

-

Locate the Cell Illustrator jar file ci-application.jar in the
installation folder and execute it by double cliking it or by typing thecommand java –jar ci-application.jar in the console

Licensing Information

During the first start of Cell Illustrator you will be asked to enter a license. If
you do not have the license please contact your Cell Illsuatrtor distributor. Cell
Illustrator supports two types of licenses, either a license string (plain text) or alicense file. If, you have a license, please choose its type and enter it in the License
Dialog box.
2.4

CI Memory Customization

By default the maximum memory for CI Desktop is set to 1024MB (1GB).
You can change the maximum memory by editing the ci start scripts: ci50.bat or
ci50.sh© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 5 of 85

2.5

Uninstallation
To unistall Cell Illustrator, please do one of the following:-

Locate the Uninstall Cell Illustrator short-cut. It is added to
the Start Menu, or-

Go to the Cell Illustrator installation folder, then to the Uninstaller
subfolder and execute the uninstaller.jar by double cliking it orby typing the command java –jar uninstaller.jar in the
console© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 6 of 85

3 Cell Illustrator elements
CI is designed to model biological pathways using three types of abstractions:entities, processes and connectors.
3.1

Concept Overview

The modeling and simulation engine of Cell Illustrator™ is based on an
extension of the Petri-net methodology. A Petri-net models a system of conditions
and instantaneous events, their dependencies and mutual synchronization. The Hybrid
Petri-net with extensions (HFPNe) employed in the Cell Illustrator adds the notion ofcontinuous and generic processes and quantities, which are essential to the descriptionof biological systems [10]. The following table presents a summary of the Petri-net
model elements, their explanations and equivalent biological meanings.
Table 1 HFPNe elements.

Hybrid Functional Petri Net Elements
Petri Net Component

SymbolBiological Equivalent
Discrete Entity
A countable biological component or
event that is quantified or represented byan integer, e.g. the number of molecules
locating at the membrane, an event that
describes whether a protein is binding to
DNA or not.

Discrete Place
A discrete place holds tokens represented
as a non negative integer.

Quantity
The number of entity items present, e.g.
the number of molecules binding toDNA, ON/OFF state of a gene
expression, etc.

Tokens
A token is a unit held in a place.

Discrete Transition
Discrete Process

A discrete transition is a discrete processthat consumes tokens from their input
places and produces tokens into their
output places. The input and output
places are connected to a transition byarcs. A transition “fires” and the process
is invoked if all the conditions for firing
are met. Every transition has a delay
time for firing.

A biological reaction that converts
quantities in discrete entities intoquantities in other discrete entities. A
switching mechanism can be also handled
as a discrete process which receives somesignals and sends out signals for apathway.

Continuous Entity
Continuous Place

A biological entity like a concentration ofa protein, enzyme, or ion, etc. thequantity of which can be represented as areal number.

A continuous place holds a non negative
real number.

Concentration, Quantity

Marking
A variable representing the state of aplace.

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

m

System status representing quantity orconcentration of proteins, enzymes andions.

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Continuous Transition

Continuous Process

A continuous transition is a continuous
process consuming quantities of its input
places and producing quantities into its
output places. It defines a continuous
flow that is specified by a speed of firing[v].

A biological process or a reaction liketranscription, translation, or enzymatic
reaction, which consumes some entities’(inputs) quantity and produces others(output), according to a rate formula.

Generic Entity

Universal Place
A universal place can handle a list ofvariables of various types (double,
Boolean, string, etc.).

A biological/chemical/physical entity thatis not pre-defined. May contain moredata types like string that is used torepresent DNA sequence.

Universal transition

Generic Process

A transition for a universal place. A
universal transition allows the arcs
connecting with this transition to use theirfunctions (firing speeds).

A complicated biological/chemical/
physical process, e.g., a translation
process.

Arc/Connector
Process Connector
Normal Arc
A directed arc. Connects a transition to aplace or connects a place to a transition.
An arc has a weight.

Inhibitory Arc
Connects a place to a transition. An
inhibitory arc disables a transition. If thequantity in its source place is greater than
the weight of the arc, the transition
cannot fire.

Connects input entities to a process andprocess to output entities. Its weightparameter specifies an activationthreshold. Process Connector allows
quantities flow in the model.

Inhibitory Connector
Prevents a process from being activated.
Inhibitory Connector facilitates themodeling of the inhibition/competition
process.

Association Connector
Test Arc
Connects a place to a transition. A testarc does not consume any quantity of thesource place by firing.

3.2

Connects entities without causing
concentration change. Defines which
entities are associated with each other,but not as inputs or outputs. Association
Connector allows the modeling of
enzymatic reactions as well as othercatalytic processes.

Entities

Entities are abstract elements that can represent any type of biological concepts
like mRNAs, DNA, proteins, ligands, and compounds. They can also represent
cellular structures like mitochondria, cell nuclei, cells or biological phenomena liketranscription and translation.
An entity (Figure 1) contains a value which is interpreted as its quantity orconcentration. You can input a more meaningful name and value for the entity. Also,
each entity has an associated variable (e1, e2, e3) representing its quantity (30, 123.2,
ATGC, respectively). The mathematical equations used for simulation are expressedin terms of these variables.
© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

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There are three types of entities: discrete entity, continuous entity and generic
entity.

Figure 1

Discrete entity holds an integer quantity (Integer or Long). In contrast, acontinuous entity has a continuous (e.g. real / fractional) number as its quantity(Double). This type of entity is used to represent concentrations, e.g. the number ofions or enzymes. Other type of entity is a generic entity. It holds string or logicalvalue (String or Boolean).
3.3

Connectors

Connectors are used to connect entities to processes (input connectors) orprocesses to entities (output connectors).
There are three types of connectors (Figure 2): process connectors, inhibitory
connectors and association connectors. An input connector can take all three types. In
contrast, an output connector is always of the process type. Correspondingly, all typesof connectors can connect input entities to a process. In contrast, only a processconnector can connect a process to an output entity.

Figure 2

The threshold is an input connector’s parameter. It can be a value or a script,
e.g. m/10. This parameter is used to define the minimum value of the input entities
needed for the activation or deactivation of the linking processes.
A process and an association connector become activated if the threshold of
the connector is smaller than the value of input entity. On the other hand, inhibitory
connectors are activated when input entity is less than this threshold. Inhibitory
connectors are used to model repression (see Section 4.9). Association connectors areused to model the situation when entities and processes need to be linked together, but
nothing is produced or consumed. They do not allow transport of quantities. They areused when the input entities concentrations do not change (see Section 4.10).
© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 9 of 85

3.4

Processes

Processes (Figure 3) define the rate of entity value changes and interactions
among entities. Processes are used to model biological reactions such as enzymatic
reactions or protein complex formation processes. Processes can take multiple inputs(e.g. consume different entities to produce new entities) and have multiple outputs(e.g. produce different entities, such as breaking down sacharose into glucose andfructose). Also, like entities, processes can be continuous (continuous processes),
discrete (discrete processes) or generic (generic processes), depending on the input,
output and timing. Only generic processes are able to handle generic entities.

Figure 3
In general terms, if all input connectors of a process are activated the process
is said to be enabled (see Section 6.1). When a process is enabled, the reaction that it
represents will fire (will be executed) immediately or after a certain time interval.
This will cause a change in value for all connected entities. For example, if a processrepresents the reaction of a protein precursor which turns into an activated protein byphosphorylation, the decrease of the precursor protein and the increase of the active
protein on account of the speed of phosphorylation is simulated once the process fires.
The exact conditions for enabling a process depend on its Firing Style parameter andare described in detail, along with other process parameters, in Section 5.4.2.
The process type determines its calculation style. The continuous process has
the speed calculation style - the process calculates the speed of production or
consumption of the entities. On the other hand, the discrete process has the add style- the process calculates the concrete values that should be added/subtracted to theentities. Finally, the calculation style of the generic process is update, i.e., the process
calculates a concrete value that will replace the entity value. The Kinetic Script
parameter specifies how to calculate the value change of connected entities in each of
the three cases. See the Reference Manual for details.
Each discrete and generic process has an additional Delay parameter that canbe a constant or a function. The firing of a discrete process occurs abruptly with thedelay associated with the process. The same applies to generic processes. The activityof continuous processes is different: The firing of a continuous process occurs
continuously. Consequently, a continuous process does not hold the Delay parameter.

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

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To employ generic processes one must use the Pnuts scripting language. Useof Pnuts is briefly described in Section 5.6.
Another important parameter associated with a process is its Kinetic Style. If
the process kinetic style is Custom then the value change after the process fires is thesame for all connected input/output entities. For all other kinetic styles, the valuechange at each entity depends on connector-specific parameters such as Connector
Stoichiometry and Connector Speed. Thus, it may be different for different
input/output entities.
3.5

Element Combinations

All combinations of discrete and continuous CI elements are presented inTable 2 and Table 3.

Table 2 Output Connectors

Transport quantityv(t) through theConnector at d(t)intervals.

Not Allowed.

Transport quantityv(t) through theConnector at eachd(t) interval.

Sequentiallytransport quantityv(t) through theConnector.

Not Allowed.
The variable m(t) represents quantity or concentration of the entity at time t.
The variable w(t) is the threshold of the corresponding connector. The constant d(t) isthe delay time of the corresponding process and the function v(t) is the speed of thecorresponding process at the time t. Note that CI checks and prohibits the connections
that are not allowed during model creation.

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

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Table 3 Input Connectors

Process is enabled
at intervals of the
time
d(t)
while
m(t)>w(t) holds.

Not Allowed

Process is enabled
at intervals of the
time
d(t)
while
m(t)>w(t) holds.

Process is enabledat the speed of v(t)while
m(t)>w(t)holds.

Process is enabled
at intervals of the
time
d(t)
while
m(t)>w(t) holds, but
no quantity change.

Process is enabled
whilem(t)>w(t)holds,
butnoquantity change.

Process is enabled
at intervals of the
time
d(t)
while
m(t)>w(t) holds, but
no quantity change.

Process is enabled
whilem(t)>w(t)holds,
butnoquantity change.

Processisnotenabledwhilem(t)>w(t) holds.

Processisnotenabledwhilem(t)>w(t) holds.

Processisnotenabledwhilem(t)>w(t) holds.

Processisnotenabledwhilem(t)>w(t) holds.

3.6

Discrete Elements Example 1

Figure 4

The model in Figure 4 consists of two discrete entities e1 and e2, and thediscrete process p1 between them. The entity e1 is connected to p1 with the input
process connector c1. The process p1 is connected to e2 with the output connector c2.
The kinetic style of the process is Custom. The entities e1 and e2 have the initialvalue 10 and 0, respectively. The input connector c1 has the Threshold parameter
equal 2. The threshold of an input connector controls the activity of process p1. The© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 12 of 85

process p1 has the delay parameter equal 1 and the add parameter equal 2. This model
can be used for auto catalytic reactions, e.g. protein auto-phosphorylation.
In the HFPNe theory, time is measured in virtual time units called Petri nettime (pt). At time 0, the value of e1 is 10 and the threshold of c1 is 2, and the process
p1 is enabled. When all input processes are enabled, the connected discrete process isreally executed after the delay time of the process. In this example, the delay
parameter of the process p1 is 1. Therefore, the process is executed at time 1.
Consequently, at time 1 the entity e1 decreases and e2 increases by the speedof process p1, i.e., 2. Thus, at time 1, the value of entity e1 and e2 becomes 8 and 2,
respectively.
At time 4, the value of the entity e1 becomes 2 and the process p1 cannot beenabled because the value of e1 is less or equal than the threshold of c1. At that time,
the process p1 cannot be enabled and e1 and e2 reach a stable state. The results of thesimulation are presented below (Figure 5).

Figure 5

3.7

Discrete Elements Example 2: Connector Speed

In the previous model, the values of e1 and e2 increase and decrease by thesame amount (2) at each time interval because their value depends on the add value ofthe process p1. However, modeling differing increase and decrease in the value ofconnected entities is sometimes necessary, e.g., when a monomer becomes a dimer ora trimer and vice versa.
To deal with this type of process, set the process kinetic style to connector
custom and then define the value changes of the connected entities separately.

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 13 of 85

Figure 6

The example in Figure 6 demonstrates the use of the connector custom kinetic style.
The difference between the previous example and the model in Figure 6 is as follows:(i)

The add parameters of connector c1 and c2 are 2 and 1, respectively.

At time 0, the value of e1 is 10 and the threshold of c1 is 1 and the process p1
is enabled. After the delay of p1, i.e. 1, the process is executed. The add valueof c1 and c2 are 2 and 1, respectively. Thus, the value of e1 and e2 becomes 8
and 1 at time 1, respectively. At time 4, the value of e1 and e2 are 2 and 4,
respectively. At that time, the process p1 is not enabled and e1 and e2 reach astable state. The results of the simulation are presented below (Figure 7).

Figure 7

3.8

Continuous Elements Example 1

In the previous two examples, discrete entities and discrete processes havebeen used. However, in many cases, entity values should be treated as continuous
variables rather than discrete variables. The model in Figure 8 has been created byconversion of the discrete model in Section 3.6.

Figure 8© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 14 of 85

More specifically, the differences between the models in Figure 4 and in
Figure 8 are as follows:
Step 1: The type of entities e1 and e2 is changed from discrete to continuous.
Step 2: The type of process p1 is changed from discrete to continuous.
Step 2: The delay of process p1 is changed from 1 to 0.
All other parameters are the same as in the discrete model in Section 3.6. As
described in Section 3.6, the threshold parameter of the input connector works like thethreshold of the activity of process p1. In this example, at time 0, the value of e1 is 10
and the threshold of c1 is 2 and the process is enabled. When all input connectors areenabled, the connected continuous process is executed with no delay.
Since p1 is connected to e1 with the quantity m1, and its output is connected
to e2 with the quantity m2, the equations for quantities change are:dm1 dm2


1
dt
dt
At time 4, the value of e1 is the same as the threshold of c1, i.e. 2. At that time,
the process p1 cannot be enabled and e1 and e2 reach a stable state. The results of thesimulation are presented below (Figure 9).

Figure 9

The same conversion process can be applied to the discrete model in Section
3.7 (Figure 10).

Figure 10

The equations for quantity changes are:© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 15 of 85



dm2
dm1
1
2
dt
, dt

The results of the simulation are presented below (Figure 11).

Figure 11

3.9

Continuous Elements Example 2
In the previous two examples, discrete examples were converted into
similar models with continuous elements. For kinetic based modeling, one should
use a continuous model rather than a discrete one.
In the following model (Figure 12), the consumption ratio of e1 is
proportional to the value of e1, i.e. the speed of process p1 follows the mass action
kinetics.

Figure 12

The difference between the present model and the model in Section 3.8 isthat the speed of p1 is changed from 1 to 0.05*m1. In this model, process p1
continuously transports entity e1 to e2 at the rate of 1/20 of e1’s concentration m1.
Since p1 is connected to e1 with quantity represented by m1, and its output isconnected to e2 with quantity m2, the equations for quantities change are:dm1 dm2
 0.05 * m1
dt
dt

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 16 of 85

The value of entity e1, i.e. m1, decreases while the value of entity e2, i.e.
m2, increases during simulation. The equation (0.05*m1) of process p1 governs
this change of value per unit time (see Section 6.1 for Simulation Details).
The results of the simulation are presented below (Figure 13).

Figure 13

If consumption and generation speed should be different, the Connector
Custom kinetic style should be used and the corresponding speed/add parameters
for connectors c1 and c2 should be set. In Figure 14, the speed of c1 and c2 is
0.05*m1 and 0.1*m1, respectively.

Figure 14

The results of the simulation are presented below (Figure 15).

Figure 15© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 17 of 85

4 Biological Process Modeling with Cell Illustrator
This section briefly explains how to simulate major biological processes with
CI. The examples have been designed to help the novice user to become familiar with
the Hybrid Petri Net modeling method and notation.

4.1

Degradation

In a cell, nearly all mRNAs and proteins fragment into parts at certain speeds.
This fragmentation activity is called degradation. A model of degradation can becreated with the following steps.
Step1: Create an entity and change its name, e.g. p53, and set its value, e.g. 10.
Step2: Create a process and set its name, e.g. “degradation”.
Step3: Connect the p53 entity to the degradation process with the processconnector.
Step4: Set the degradation speed by editing the Kinetic Script parameter of thedegradation process. For the discrete process the default speed is 1.0.
For the continuous process, set the Kinetic Style to Custom and thenedit the Kinetic Script (speed).
In Step1 and Step2 the following combinations are possible (Figure 16): a
discrete entity and a discrete process, a continuous entity and a continuous process, acontinuous entity and discrete process

Figure 16

The results of the simulation are presented below (Figure 17).

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 18 of 85

Figure 17

If the degradation speed depends on the value of p53 protein (say m), in Step 4
the speed should be made dependent on m (Figure 18).

Figure 18

The results of the simulation are presented below (Figure 19).

Figure 19© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 19 of 85

4.2

Translocation

In a cell, mRNA and proteins move from one compartment to others, e.g. from
nuclei to cytoplasm and vice versa. A model for this can be created with the following
steps.
Step1: Create two entities and change their names, e.g. p53_nuclei andp53_cytoplasm and set their values, e.g. 100 and 0.
Step2: Create a process and set its name, e.g. translocation. Connect the
p53_nuclei to the process and connect the process to p53_cytoplasm.
Step3: Set the translocation speed by editing the Kinetic Script parameter of
the translocation process. For the discrete process the default speed is1.0. For the continuous process, set the Kinetic Style to Custom andthen edit the Kinetic Script (speed).
In Step1 and Step2, possible combinations of (p53_nuclei, translocation,
p53_cytoplasm) triple are (discrete, discrete, discrete), (discrete, discrete, continuous),(continuous, discrete, continuous), (continuous, discrete, discrete) and (continuous,continuous, continuous) (Figure 20).

Figure 20

The simulation results of the discrete model are presented below (Figure 21).

© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 20 of 85

Figure 21

If the translocation speed depends on the quantity of p53_nuclei (say m1), as
in the degradation example, Section 4.1, the process speed should be made dependent
on m1/10 in Step3. (The factor of 1/10 was used in the example in Figure 22.)

Figure 22

The results of the simulation are presented below (Figure 23).

Figure 23

4.3

Transcription

Transcription modeling can easily be done using CI. A model can be createdwith the following steps.
Step1: Create a process and set its name, e.g. transcription.
Step2: Create an entity and change its name, e.g. mRNA_p53.
© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 21 of 85

Step3: Connect the transcription process to mRNA_p53 entity with the processconnector.
Step4: Set the transcription speed by editing the Kinetic Script option of thetranscription process. For the discrete process the default speed is 1.0.
For the continuous process, set the Kinetic Style to Custom and thenedit the Kinetic Script (speed).
In Step1 and Step2, possible combinations of the pair (transcription,mRNA_p53) are (discrete, discrete), (discrete, continuous) and (continuous,continuous) (Figure 24).
Cell Illustrator facilitates the modeling of the path DNA  mRNA  Protein
not only as a concentration function of DNA, mRNA and Protein. Global transcription
and translation mechanism can be established with a string input (DNA sequence),
intermediate product (proper mRNA) and product (Protein). This mechanism canproduce simultaneously many proteins from different DNA sequences.

Figure 24

The simulation results of the discrete model the are presented below (Figure 25).

Figure 25

Normally, mRNA synthesis is associated with its degradation. The models
presented below (Figure 26) combine the transcription process with the degradation
process in Section 4.1 (The same speed m/10 is applied.)© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 22 of 85

Figure 26

The results of the simulation are presented below (Figure 27).

Figure 27

4.4

Complex

In a cell, a set of proteins binds and creates a complex. A model for this can becreated with the following steps.
Step1: Create three entities and set their names, e.g. p53, mdm2, p53_mdm2,
and set their values, e.g. 100, 50, 0.
Step2: Create one process and changes its name, e.g. complex.
Step3: Connect p53 entity and mdm2 entity to the complex process. Connect
the combine process to the p53_mdm2 entity.
Step4: Set the combine speed by editing Kinetic Style and Kinetic Script
options to values Custom and 1.0.
In Step1 and Step2, 9 combinations of element types in the pattern (p53,
mdm2, p53_mdm2, complex process) are allowable, i.e., (discrete or continuous,
discrete or continuous, discrete or continuous, discrete) and (continuous, continuous,continuous, continuous) (Figure 28).
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Figure 28

The simulation results of the continuous model are presented below (Figure
29).

Figure 29

If the complex speed strictly depends on the value of p53 (m1) and mdm2(m2), the speed of the complex process needs to be (m1*m2)/400 (the factor 1/400 is© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

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an example) for the model where all elements are of the continuous type as in Figure
30.

Figure 30

The results of the simulation are presented below (Figure 31).

Figure 31

4.5

Tetramerization

In a cell, some proteins create a self-complex under certain conditions. For
example, p53 can create a homotetramer. The process of creating tetramers is called
tetramerization. The three examples below show how various kinetic styles processes
can be used for the modeling of tetramerization. The speed of tetramerization islinearly dependent on the mass of monomers in the first example. In the second
example, stochastic perturbation is introduced. Finally, in the most advanced example,
tetramization is defined by a script.
A simple model of tetramerization can be created using the following steps:
Step 1: Create two entities and set their names, e.g. p53 (monomer) andp53 (tetramer). Set the initial value of the monomer to 10.
Step 2: Create a process and set its name, e.g. “tetramerization”.
Step 3: Connect the p53 (monomer) entity to the process with a processconnector.
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Step 4: Connect the process to the p53 (tetramer) entity with a processconnector.
Step 5: Set the Kinetic Style of the process to Mass and set c1 Stoichiometry
to 4. Leave the default values for the other parameters of the process: Coefficient1 0.01, Coefficient2 - 0.1 and c2 Stoichiometry - 1.

Figure 32

With the model, as in Figure 32, the speed of the tetramerization processdepends on the value of p53 (tetramer). To make the process of tetramerization fasterthe Coefficient1 value should be increased. To change tetramerization into thedimerization process the “c1 stoichiometry” value should be changed to 2. Moredetails on parameters related to the Mass kinetic style can be found in the section . 3.4
Processes.
The results of the simulation are presented below (Figure 33).

Figure 33

4.6

Stochastic Tetramerization

Normal biological processes (e.g. dimerization or tetramerization) are notdeterministic but stochastic. To improve modeling of the biological processes one canuse the Stochastic Mass kinetic style of the process. Starting from the previous model,
the stochastic tetramerization process can be modeled as follows:
Step 6: Change the Kinetic Style of the process to Stochastic Mass. Set theStandard Deviation to 0.2. This parameter controls the distribution of therandomly generated process speed.
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Step 7: Make sure that other parameters are the same as in the previous
Tetramerization example, i.e. c1 Stoichiometry is 4.

Figure 34

In the model, shown in Figure 34, the speed of the tetramerization processrepresents stochastic behavior. Parameters related to Stochastic Mass kinetic style aredescribed in section 3.4 Processes.
The example results of the simulation are presented below (Figure 35). Notethat subsequent simulation runs will give different results due to the stochastic natureof the model.

Figure 35

4.7

Custom Tetramerization

Tetramerization can also be defined by a custom formula: In the example in
Section 4.5, the process speed depends on the p53 (monomer) in a linear way, but one
can change it into a quadratic function by writing a script. The example in 4.5 could
be modified as follows:
Step 6: Change the Kinetic Style of the process to Connector Rate and set theRate parameter as 0.001*m1*m1.
Step 7: Make sure that other parameters are the same as in the previous
Tetramerization example, i.e. c1 Stoichiometry is 4.

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Figure 36

In the model in Figure 36, the speed of the tetramerization process is
proportional to the square of p53 (monomer).
The results of the simulation are presented below (Figure 37).

Figure 37

4.8

Separation

In a cell, binding proteins are sometimes separated with proteins. A model canbe created here with the following steps:
Step1: Create three entities and set their names, e.g. p53, mdm2, p53_mdm2,
and set their value, e.g. 50, 0, 50.
Step2: Create one process and set its name, e.g. separate.
Step3: Connect the p53_mdm2 entity to the separate process. Connect the
separate process to the p53 entity and the mdm2 entity.
Step4: Set the separate speed by editing Kinetic Style and Kinetic Script
options to values Custom and 1.0.
Again, 9 combinations of element types in the set (p53, mdm2, p53_mdm2,
separate process) are possible in Step1 and Step2, i.e. (discrete or continuous, discreteor continuous, discrete or continuous, discrete) and (continuous, continuous,continuous, continuous) as in Figure 38.

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Figure 38

The results of the simulation are presented below (Figure 39).

Figure 39

If the speed of the separate process strictly depends on the value of p53_mdm2(say m1), this can be expressed by a respective equation defining the speed parameterof the process (In Figure 40, m3/20 for the model where all elements are of thecontinuous type).

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Figure 40

The results of the simulation are presented below (Figure 41).

Figure 41

For models based on discrete elements, it is difficult to create a model that
fulfills this condition.
4.9

Inhibition

A specific drug sometimes inhibits activities of transcription. The transcription
model in Section 4.3 is modified with an inhibitor activity as follows.
Step1: Create an entity and set its name, e.g. doxorubicine.
Step2: Create a process and connect it to the doxorubicine entity with theprocess connector.
Step3: Connect the doxorubicine entity to the transcription process with the
inhibitory connector.
Step4: Do not change the threshold of the inhibitory connector (the default
value is 0).
The type of the doxorubicine entity can be either discrete or continuous. With
the model in Figure 42, one can simulate the situation where the concentration of
doxorubicine is growing. If the inhibition connector threshold is set to 0 for
continuous processes the translation will be stopped immediately after the first
Sampling Interval. For a discrete process Translation will be stopped after the firststep, as during the first step process (p1) is enabled.
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Figure 42

The results of the simulation are presented below (Figure 43)

Figure 43

If the threshold of the inhibitory connector is modified to 5 (Figure 44) the
results will be different. The results of the simulation are presented below (Figure 45)

Figure 44© 2002-2017 Human Genome Center, Institute of Medical
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Figure 45

As in Figure 43 and Figure 45, if doxorubicine is treated the transcription of p53 isinhibited. The inhibitory activity will be adjusted with the threshold of the inhibitory
connector.
4.10 Catalysis
Enzymes catalyze a variety of chemical reactions. The model of an enzymatic
reaction can be created with the following steps. The first three steps are similar to thetranslocation process in Section 4.2.
Step1: Create two entities and change their names, e.g. p53 and p53-P and set
their values, e.g. 10 and 0.
Step2: Create a process and set its name, e.g. catalysis. Connect p53 to theprocess and connect the process to p53-P.
Step3: Set the catalysis speed by editing the speed option of the catalysis
process. Set Kinetic Style to Custom and Kinetic Script (speed) to 1.0.
Step4: Create one entity and change its name, e.g. CAK.
Step5: Create a process and connect it to the CAK entity with the processconnector. Set kinetic style to Custom and kinetic script (speed) to 1.0.
Step6: Connect the CAK entity to the catalysis process with the association
connector.
Step7: Change the threshold of the association connector to 1(the default valueis 0).
The type of CAK entity is continuous. With the model in Figure 46, one cansimulate the situation where CAK (enzyme) concentration is growing. If theassociation connector threshold is set to 1 the catalysis process will be enabled when
the CAK entity value reaches 1 level.

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Figure 46

The results of the simulation are presented below (Figure 47)

Figure 47

If the threshold of the association connector is modified to 5 (Figure 48) the
results will be different. The results of the simulation are presented below (Figure 49)

Figure 48© 2002-2017 Human Genome Center, Institute of Medical
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Figure 49

In the presented reaction, CAK influence on the model is enabling/disabling
the reaction. The CAK activity will be adjusted with the threshold of the association
connector.
However, in most of the systems, the reaction rate depends on enzyme
concentration and substrate concentration. In the example in Figure 50, the speed of
the catalysis process is defined with the equation (m1*m2)/10. The model is made ofcontinuous type elements and the threshold of association connector is reset to 0.

Figure 50

The results of the simulation are presented below (Figure 51).

Figure 51© 2002-2017 Human Genome Center, Institute of Medical
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5 Cell Illustrator User Interface and Model Creation
At Cell Illustrator startup, a single main window pops up. You can create,
open, save and print a biological pathway model file using options in the File menu
(Figure 52).
This window has the Menu Bar at the top that allows you to manipulate themodel diagram.

Figure 52

To create a new model, the first step is to open an empty canvas window for themodel, which can be accomplished by either:
1. clicking on the top iconin the left toolbar

2. clicking on the File in the Menu Bar and selecting the “New” option

5.1

Add Elements

To add a new element—an entity or a process, select one of
,
, , ,
and
icons from the top toolbar, and then click on the canvas workspace where
you want to place it. You may continue and place more components of the selected
type on the canvas.
Alternatively, you can press the right mouse button anywhere on the canvas
and choose the desired element from the popup menu using Insert Entity/Insert
Process function. The new element will be placed at the center of the active canvas.
Lastly, you can select a predefined element in the Biological Elements frame(see Section 5.5.1).
Each entity has three labels associated with it for you to edit by double
clicking (see Figure 1). On the upper left is the Name of the entity for associating
biological concepts with the entity. The upper right label is the Variable representing
this entity. It is used in mathematical equations to define the rate of reaction in themodel. The lower left label represents the current value of the entity. You can edit it© 2002-2017 Human Genome Center, Institute of Medical
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to enter the initial value for the selected entity. Note that for discrete entities, thisvalue must be an integer.
A process can have up to four associated labels (see Figure 3). At the upper
left is the Name of the process. The lower right label is the Kinetic Script of theprocess. The upper right label is the Firing Style of the process and the lower leftlabel is the Delay of the process.
To add a connector to link two elements, select a connector type from ,and icons in the Draw toolbar above the canvas pane and then click on the element
where the connection originates. Next, click on the element you want to connect to.
The connector will be automatically drawn to connect the two elements. Note that aconnector links entities to processes, not entities to entities or processes to processes.
If you draw a connector between two entities, a process with default parameters isinserted between them. Also, discrete entities cannot connect to continuous processes(see Element Combinations in Section 3.5). Generic entities can only connect togeneric processes. Each connector has up to three labels: above the connector is its
name, below is its firing style, and below the connector is its threshold (if thresholdfiring style is enabled) or the rule (if rule firing style is enabled) (see Figure 2). The
threshold is a minimum value for the activation of the connector (see Simulation
Concept, Section 6.1).
5.2

Modify a Model
The user can move any element within the model (i.e., change its location).
To carry out this task, click on the Selection Modebutton from the Draw
toolbar. Then, click on the element in the canvas and drag it to move it. You canalso drag the mouse to select multiple elements and then right click the mouse tobring up a popup menu which allows to you to arrange, copy, cut, duplicate ordelete the selected elements. A mouse right click on a selected single element willbring up a customized popup menu with additional options to modify the element.

The Draw and Element toolbars contain buttons for frequently performed editing
operations. This includes:
“Insert Frame”— Create a background frame.

“Insert Text” — Annotate the model.

“Insert Image” — Load an image and insert the image to the canvas.

“Zoom In” — Zoom in on the canvas.

“Zoom Out” — Zoom out from the canvas.

“Reset Zoom” — Restore the default models magnification.

“Fit In Canvas” — Restore the default models magnification.

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




5.3

“Group” — Group together chosen elements to treat them as one
for editing .
“Ungroup” — Undo a group operation.
,,
Text Color.

Set Color tools:

Fill Color,

Line Color or“Set Stroke” — Change the stroke pattern of selected elements.

“Toggle Grid” — Turn the background grid on or off“Toggle Antialiasing” — Turn on or off antialiasing

Change Element Properties

For additional editing of the properties of elements in the model, the Property
Frame toolbar at the far right of the window is provided. Note that the Window |Show Frame menu contains all options provided in this toolbar.
The actions for each button are as follows:“Element Lists” — show the Element Lists frame (see Section 5.4).
“Element Settings” — show the Element Settings frame (seeSection 5.4).

“Biological Properties” — show the Biological Properties frame“External References” — show the External References frame (seeSection 5.4).

“Navigator” — show a small view of the entire model to navigate
the model in the canvas window (see Section 7.1).

“View Settings”— show the View Settings frame. In this frame theuser can customize the global view settings of the active canvas, e.g.,show and hide entity labels.

“Chart Settings” — show the Chart Settings frame to create timeseries plots (see Section 6.2.3).

“Simulation Settings”— show the Simulation Settings frame tochange simulation parameters (see Section 6.2).

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5.4



“Simulation History” – show the Simulation History frame. The
frame contains the list of all simulation log files for the active model file(see Section 6.3.2)

“Graph Layout” – show Graph Layout frame to set up elements on
the canvas in the specified way.

“Path Search Results” — show the Path Search Results in a tabular
view (see Section 7.2).

“Comments” — show the comments of the selected element in atabular view

Element Settings Frame and Element Lists Frame

The frames that show the details about the variables and equations in themodel are the Element Lists (see Figure 53, Figure 55, and Figure 57) and the
Element Settings (see Figure 54, Figure 56 and Figure 58). They can be opened either
by selecting the items Element Settings and Element Lists respectively in Window |Show Frame menu or by clicking on the buttons
andin the Property Frame
toolbar.
The Element Lists frame consists of the following sheets: the Entity, Process
and Connector Sheet as well as the Fact, Fact Edge and Group Sheet. Each sheet
contains a table of all elements of the given type (entity, process, connector, fact
vertex, fact edge or groups respectively) in the active canvas and their main properties.
A selection made in the Element List will be immediately reflected in the canvas, andvice versa. You can easily change a property of an element by double clicking on thecorresponding cell in the table. Note that the columns which can be edited have awhite background while the columns that cannot have a gray background. To change
an element property displayed in a gray-out column, you need to select acorresponding element in the canvas and open the Element Settings frame.
The Element Settings frame displays all the properties of a single element
selected in the canvas. The frame may display, depending on the type of selected
element, a list of various properties divided in categories: Simulation, View, Shape,
Image, Biological Properties, Custom Biological Properties, etc.. Any modifications
made in the Element Settings will be immediately reflected in the Element Lists andthe canvas, and vice versa.
5.4.1 Entities
For entities, several properties are available for editing in both frames (seeFigure 53 and Figure 54). A default setting is automatically displayed if you have notentered any value. You can change the name of the entity to reflect its biologicalmeaning, and you can also enter the initial value of the entity. The current value ofthe entity is displayed once simulation data is available. To show/hide the selected© 2002-2017 Human Genome Center, Institute of Medical
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element in a simulation output file, check the Visible box. Clicking on the column
header you can sort or filter the table. You can also control if a given column(property) should be visible or hidden in the table by a right-click on the column
header and choosing the Select Visible Column item from the popup menu.

Figure 53

If an entity is selected in the canvas and the Element Settings frame isactivated, the properties of the selected entity are presented there (Figure 54). The
type of entity cannot be edited in Element Lists. However, it can be changed in the
Element Settings frame. A continuous entity can only have the Double value type,while a discrete entity be it Integer or Long or a generic entity can have the value type
String or Boolean. The Maximum Value and Minimum Value fields define theallowable value range. The display properties associated with the selected entity canbe modified in the View and Shape categories.

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Figure 54© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

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5.4.2 Processes
In the Process List in the Element Lists (Figure 55), you can change the nameof the process to reflect its biological meaning. Also, you can set the Kinetic Script
and Delay Script property. The Kinetic Script and Delay Script fields define,respectively, the speed / add / update function and delay parameter of the processes.
The Kinetic Value and Delay Value columns display current simulation values.

Figure 55

If a process is selected in a canvas, the Element Settings frame displays theproperties of the selected process (Figure 56).

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Figure 56

The major process parameters displayed in the Process Sheet are Type, Calc
Style, Firing Style, Activity, and Kinetic Style. The Calc Style property determines its
Type , which defines whether the process is discrete, continuous or generic and.
One can choose between two Firing Style settings – “and” and “or”. For aprocess with the default “and” firing style, if all input process connectors andassociation connectors of the process are activated and no input inhibitory connectorof the process is activated, the process is enabled. For a process with the “or” firing
style, the process is enabled if at least one of Association or Inhibitory input
connector fulfills its condition and all input process connectors fulfill their respective
conditions.
The Kinetic Style defines the method for calculating the values of connected
entities. The property can have the following options: Custom, Connector Custom,
Mass, Stochastic Mass, Connector Rate, Michaelis-Menten, Hill Function. Each stylerequires a specific set of parameters that you need to enter at the bottom part of the
Element Settings frame. For example, you must specify the Coefficient1, Coefficient2,
Standard Deviation and Connector Stoichiometry for the Stochastic Mass style. The
most general style is Custom. If a process has the Custom kinetic style, you need to
specify directly its Kinetic Script, i.e., the formula that governs the value change. For
other kinetic styles, the kinetic script is predefined and you can only set the values ofparameters in the equation.
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The Delay option is enabled for a discrete or continuous process (see Section
3.4) and it takes a positive real value or zero. A discrete process will fire just after its
delay time if the process is still active. Otherwise, it can lose its chance to fire. The
delay is specified in Petri net time [pt].
By default, the Activity property of a process is set to true. However, you canwrite a script in the Activity field to turn the process on and off in the course of asimulation. For an example on how to write a script, see Section 0
In the View and Shape category, you can modify the display properties
associated with the process.
5.4.3 Connectors

Figure 57

In the Connector List of the Element Lists frame, the Name, Type, From, To,
Firing Style, Firing Script and Visible properties are displayed (Figure 57).
When a single connector is selected in a canvas, the Element Settings frame
displays its properties (Figure 58).
The major properties that you can edit are CSO Class, Connector Firing Style,Firing Threshold and Firing Rule. The CSO Class defines the connector type, which
can have the values process, inhibitory or association. The Connector Firing Style
option defines how the condition for activating the connector is calculated. (It should
not be mistaken with the Process Firing Style parameter.) The Connector Firing Style
can take the Threshold, Rule or No Check value. In the Threshold style, the value ofthe Firing Threshold determines if the connector is enabled; if the current value of thesource entity is larger than the threshold then the connector is enabled. In case theConnector Firing Style is set to Rule, the behavior of the connector is defined by aboolean expression (script) in the Firing Rule field. No Check option always enables
the connector.

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Figure 58

5.4.4 Biological Properties Frame
The Biological Properties Frame displays a list of all elements of the selected
type (Entity, Process, Connector, Fact Edge, Fact Vertex, Group) along with their
biological properties and enables you to view/assign additional biological informationsuch as location (cell component), biological role, biological event, GO ID, list of
Pubmed ID’s, etc.
Similarly to the Element Lists frame, in this frame you can easily change aproperty of an element by double clicking on the corresponding cell in the table. For
selected properties such as Cell Component or Biological Event, you can select thevalues from a list of predefined choices. Please note that changing the Biological
Event of a process will assign a new graphical symbol (image) to the process on thecanvas.
Clicking on the column header you can sort or filter the table. You can also
control if a given column (property) should be visible or hidden in the table by a rightclick on the column header and choosing the Select Visible Column item from the
popup menu.
Any selection made in the Element List will be immediately reflected in thecanvas, and vice versa.
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5.4.5 External References Frame
Using this frame, you may define for each model element a list of references
to external databases or vocabularies. The defined links can then be opened andviewed in a web browser.
5.5

Biological Elements and Pathway Fragments

While you can use the menu options and toolbar buttons described in Section
5.1 to add elements, biological pathways can be created faster and more intuitively bydragging and dropping appropriate elements from the frames that support building of
pathways. These frames give the user the access to many predefined elements or
pathway fragments and the ability to insert these fragments to the active canvas.
The Library toolbar

gives a quick access to these usefulframes.

5.5.1 Biological Elements Frame
The Biological Elements frame contains the Entity, Process and Cell
Component Sheet.
The Entity Sheet contains biological elements that will contain a certain
quantity, e.g. mRNA and protein (Figure 59).

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Figure 59

The Process Sheet contains biological elements that denote biologicalprocesses, e.g. phosphorylation and translocation (see Figure 60).

© 2002-2017 Human Genome Center, Institute of Medical
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Figure 60

The Cell Component sheet contains useful pictures of cellular components, e.g.
cell and mitochondrion (Figure 61).

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Figure 61

5.5.2 Project Manager Frame
CI Project Manager is a user interface to pathway models stored onwww.csml.org web page.
In this frame, you can browse and search through pathway libraries stored onwww.csml.org and download and open the selected pathways in CI window.

© 2002-2017 Human Genome Center, Institute of Medical
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Figure 62

To open Project Manager Frame, choose

icon from the Library toolbar.

5.5.3 Parts Library Frame
The Parts Library frame can be opened with the button
from the Library
toolbar. In this frame, you can register a model file as a reusable item. Once registered,the model is accessible in the tree and the elements in the file can be inserted into theactive canvas in the same way the command File | Import | Model works. With Parts
Library, you can organize a repository of frequently reused models. Cell Illustrator
comes with several pre-registered parts to demonstrate this functionality.

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Figure 63. Parts Library usage.

5.6

Script Editor

An advanced user of Cell Illustrator may need to write a custom function to
specify changes of a property value. With the Script Editor, you can write a script todefineInitial Value property of an entity,
Activity, Speed / Add / Update and Delay of a process,
Threshold, Updater and Firing Rule of a connector.
Double click on one of the above properties in the Element Settings frame,
open the Script Editor, in which you can define the script and its language.
Cell Illustrator enables the usage of several different scripting languages, suchas: simplemath, java, java-bulk, js (javascript), pnuts. The Script Language combo
box allows for choosing a specific language for each single script.
The default language for the whole model can be set in Simulation Settings
frame. This default language will be used whenever the script language is not set in
the Script Editor.

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Figure 64

Figure 64 shows the Script Editor. In the middle, a script pad is placed where youcan edit a script either by clicking on the function buttons and selecting entities fromthe table, or by typing in text. You can check whether the syntax of your script iscorrect by pressing the Check button. If the check fails, error messages will appear ina tooltip when you place the mouse over the Check Failure message.

5.6.1 Example 1: Stochastic Behavior of Translocation Process
Many processes in a cell show stochastic behavior. In order to model a stochastictranslocation process, the model in Section 4.2 was modified by applying a script inFigure 64 to the Activity option of process p1. The results of the simulation of thediscrete model with the sampling interval equal to 1 are presented in Figure 65. The
figures compares 2 discrete processes, one randomly activated and the second always
active process. Note that the value changes of the p53_nuclei (random) entity are notsteady.

Figure 65© 2002-2017 Human Genome Center, Institute of Medical
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5.6.2 Example 2: Transcription Process with Generic Entities and Processes
Generic entities are of two value types. For example, you can use an entity that
holds a String variable to describe a translation/transcription process with a genericprocess.

Figure 66

In this example, a script shown in Figure 67 is assigned to the Updater
property of the connector.

Figure 67

Selected succeeding states of the model in Figure 66 are presented in
Figure 68.

Figure 68© 2002-2017 Human Genome Center, Institute of Medical
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5.7

Preferences Frame

Figure 69

With the Preferences frame, you can set the global properties of Cell
Illustrator. This section briefly introduces the major options available in the frame.
The frame displays a tree of option types and, and the list of available options andtheir settings for a selected part of the tree.
For example, Figure 69 shows the global settings of the figure to be used for
display of continuous entities. One can define the size of the figure, its color and otherparameters.
The values from the Entity, Process, Connector, Fact, Fact Edge sections areused as the defaults when a new element is created.
The values of Simulation Settings section, e.g. Simulation Time, Sampling
Interval, Plot Update Interval, Log Update Interval, etc. are used as the default
parameters, when a new model is created.
The Canvas options define the way how a newly created canvas will look. The
background color and the grid size can be defined here. Alternatively, you can update
major canvas options in the View Settings frame.

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5.8

Graph Layout FrameIn this frame, you can “beautify” the graph layout of the model. This function
can be especially useful when importing models from the BioPACS database orconverting models from other formats.

Figure 70

The Graph Layout frame has several options for placing elements on thecanvas such as Annealing Layout, Circle Layout, and others.

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6 Simulation
6.1

Simulation Concept

The simulation architecture of Cell Illustrator is based on the Hybrid Petri-net
with extensions (HFPNe) theory [10] and uses the concepts of entities, connectors andprocesses. With discrete elements, you can execute basic logic-based simulations.
You can also execute differential equation based simulations with continuous
elements, and combine discrete and continuous elements in more complex models.
The most advanced simulations would employ generic elements and scripting. For ashort introduction to the HFPNe theory, see Section 3.
It is easy to prepare a basic simulation just by drawing biological pathways on
the canvas. A simple model can be then turned gradually into a more advanced one byadding detailed information to HFPNe elements in the biological pathways, e.g.
kinetics data and detailed regulation rules. To build a model quickly, drag and drop
biological elements from the Biological Elements frame (Section 5.4.4) and link themwith connectors observing the connection rules listed in Section 3.5. All element
parameters are initialized with default values; the initial value of an entity is 0, thethreshold parameter of an input connector is 0, and the speed parameter of a process is
1 by default.
For any biological process to take place during simulation, the processrepresenting it needs to be enabled. A process is enabled when all the following three
conditions are satisfied; (i) all of its incoming process and association connectors areactivated, (ii) no input inhibitory connector is activated, (iii) the activity parameter ofthe process is true. (The default value of the activity parameter is true but one canwrite a script that modifies this parameter.) Once a process is enabled, the biologicalprocess can occur. Let us take a look at the simulation of a simple case: A protein
precursor is phosphorylated to be an active protein.

Figure 71

The initial value of the protein [inactive] is 10. While the concentration ofprotein [inactive] is greater than the threshold of its outgoing connector c1 which is
2.0, the connector is in the enabled state. An enabled connector in this case executes
the activation of the Phosphorylation. The protein [inactive] will be converted to theactive protein, protein [active] at the speed of phosphorylation which is V (t) =dm0/dt.
In another words, the concentration of protein [inactive] will decrease by the amount(m1-0.1*m2)*time, and the concentration of protein [active] will increase by the sameamount. Once the concentration of protein [inactive] reaches the threshold value 2,
the connector will be deactivated. Consequently, the process of phosphorylation is
terminated. The end simulation state is displayed in Figure 72.

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Figure 72

Figure 73 presents a simulation chart for this model.

Figure 73

When simulating a complex pathway, you will often need to constructmultiple entities with multiple connectors leading to one or more processes. In thesecases, only when all process and associate input connectors are activated and all
inhibitory input connectors are not activated, the process is enabled. For eachsimulation step, all the activation conditions for all processes are checked and allactivated processes are executed in a random order.
The simulation run can be logged and saved into CSV (comma separated
values) files or CI log files (CILs) – refer to Simulation Log. Saving log files gives
you the following possibilities:

View and analyze the log using a standard spreadsheet application

View and replay a simulation step by step in CI Player application –refer to Simulation Replay

Order and systematically analyze the results for a set of similar
simulations – refer to Simulation History

Creating a log file and replaying it in CI Player is recommended in the following
cases:


When the simulation may take a large amount of time, e.g., for acomplex model and accurate simulations.

When you run a set of simulations for the same model and thesimulation results may differ from one simulation run to another, forexample. For a model with random factors, or for a model with changed
parameters.

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6.2

How to Set Up and Run Simulation

6.2.1 Simulation Settings
The next step after creating a model is to set up simulation process parameters.
The Simulation Settings frame has the following options that control the simulation
flow: Sampling Interval, Simulation Time, Chart/Canvas Update Interval, Simulation
Speed, Continuous and Discrete Weak Firing, Firing Accuracy and other Execution
and Log File parameters (Figure 74).

Figure 74

During simulation, the model changes its state in a certain time sequence. In
Cell Illustrator, simulation time is measured in virtual time units called Petri net time(pt). The Simulation Time option sets the duration of the simulation process in Petrinet time. If the Simulation Time is set 1000 as in Figure 74 , the simulation lasts from
0[pt] to 1000[pt]. Note that the value of the Simulation Time parameter does notnecessarily correspond to the real time it takes to execute the simulation on yourmachine. The latter depends also on the complexity of the model, additional
simulation parameters such as the Sampling Interval and the hardware on which the
Cell Illustrator runs.
The Sampling Interval specifies the interval at which simulation steps are
executed. Again, the Sampling Interval is defined in Petri net time units. At each step,© 2002-2017 Human Genome Center, Institute of Medical
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model state changes are computed. If the interval is 0.1pt as in Figure 74, the modelchanges its state at 0.1[pt], 0.2[pt], 0.3[pt]… 1000[pt]. If the Sampling Interval
becomes smaller, the simulation becomes more accurate but calculation performancedecreases. For example, for Simulation Time 1[pt], Cell Illustrator needs to perform
100000 simulation steps if the Sampling Interval is 0.00001[pt] and only 10 steps if
the interval is 0.1[pt]. The minimum sampling interval allowable in Cell Illustrator is
0.000001 pt. You should be aware; however, that simulation with the minimum
sampling interval may take a very long time.
If the sampling interval becomes very small, the simulation result will besimilar to a differential equation model (see Appendix B).
The Canvas/Chart Update Interval defines how often canvas/charts are
updated during the simulation. These options have no meaning for simulations started
with the Max Speed Play command.
The Weak Firing option solves a problem of non-firing processes. This may
happen when the value associated with an entity is smaller than the calculated changeof this value (delta) within a given sampling interval. If this happens, the process does
not fire at all. This behavior is an inherent consequence of the classical Petri net
formalism. However, it may lead to an unexpected, unrealistic simulation behavior,e.g. when the kinetics of catalyzed reactions in biological systems are modeled – acatalyzed reaction may not “occur” at all, even if a substrate and a catalyst arepresent!
The Weak Firing can be enabled/disabled for continuous or discrete processes.
By default the this option is off which is in agreement with the Petri Net model, but issometimes difficult to understand for normal biological reactions.
Cell Illustrator has several commands to run a simulation. If the Play , Playwith Animation , Fast Play and Max Speed Play buttons in the Simulation
toolbar are clicked, the simulation will run until the end. Clicking on Step Play andStep Play with Animation
buttons executes a simulation step corresponding to one
sampling interval.
If a simulation process is started with the Play or a Play with Animation button,
the program attempts to execute the simulation at the speed 1 pt per 1 second
regardless of the Simulation Speed option in the Simulation Settings frame. On theother hand, for simulation started with Fast Play and Max Speed Play, Cell Illustrator
attempts to execute simulation at the rate defined by the Simulation Speed. For
example, if the Simulation Speed is set to 100, then the simulation will proceed at 100
Petri net time units per 1 second of real time provided that your hardware issufficiently fast. You may need to increase the Sampling Interval at the cost of result
accuracy if the simulation is too slow. The difference between Fast Play and Max
Speed Play is that the canvas is not updated until the simulation end if the latter option
is executed. Thus, you should use the Max Speed Play for time consuming models.

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6.2.2 Simulation Engine
Cell Illustrator offers two alternative engines for performing the simulation:ostandard engineosimulation engine code generator (SECG)

Both engines base on the same simulation model - Hybrid Petri-net withextensions and give the same results. However SECG executes the simulation in adifferent way: first Java source code is generated for the model to be simulated; thenit is executed as a usual Java program.
Use SECG option determines whether to use SECG or the standard engine forthe simulation execution. The SECG engine is recommended for the following twocases:-

Simulation of large models; SECG will run large models much
faster than the standard engine-

Customization/integration of simulation model; The command
Export | Simulation Source Code enables you to generate sourcecode that can be customized by a programmer and/or reused inanother software.

6.2.3 Simulation Log
The results of each simulation can be logged and saved into a CSV (Comma
Separated Values) file or CI log files (CILs). The CIL files can be viewed andanalyzed in the Cell Illustrator Player program. The CSV file can be analyzed with an
external spreadsheet application.
A CI log is an XML file that consists of two parts: the input model and a timeseries of values for the logged properties. The time series data may include:-

entity values and/or-

process/connector speed-

process state (firing, waiting, not-firing)

To track value changes for all elements, you need to:-

Set the Save Log File option to the on value (or select the Save
Log button on the Simulation toolbar)-

Define the elements to be logged in Logged Elements combo box.

-

Specify the Log Update Interval, which defines how often entity
values are saved in the simulation log.

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Logged Elements combo box enables you to define the elements to be logged
and offers the following choices:-

All Entities – Log all entity values, but do not log other elements(connectors and processes)-

All without Firing – Log entity values and process/connector
speed for all elements but do not log process state-

All with Firing – Log everything: entity values,
process/connector speed and process state for all elements-

Chart – Log these elements which are displayed on charts – seeChart Settings frame. By default, no one element is added to hechart. In such a case, nothing will be logged during simulation
and a CIL file will not be created during the simulation.

-

Log – Log these elements, which have the log option set to true.
This option is displayed in Element Lists frame. By default, theLog option of all entities, processes and connectors is off. In
such a case, nothing will be logged during simulation and a CIL
file will not be created during the simulation.

-

Chart and Log – Log all these elements, which are displayed onchart or have the Log option set.

A CIL file is created if at least one entity or process is selected for logging.
During the simulation, the data is logged at time points defined by the Log
Update Interval setting in the Simulation Settings. The log interval value needs tobe selected carefully so that an appropriate number of steps is logged during thesimulation. In general the number of steps that will be logged is given by thefollowing formula:
SIMULATION _ TIME
N _ STEPS 
LOG _ INTERVAL

It is recommended to select the log interval in such a way that the number ofsteps will be less than 100 000 steps. Logging hundreds of thousands of steps willcause the CIL file to be very large.
Since CIL stores the model in the same format (CSML) as the original project
file, you can open a log file with Cell Illustrator and edit the model. However, saving
the edited model into the original log file might erase the time series and any othersimulation history information. Therefore, you should use the Save As command of
Cell Illustrator if you would like to recover the model from a log file for further
editing.

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6.2.4 Simulation Charts
You can visualize the simulation results on time graphs. The charts candisplay time series of: entity values, process/connector speed of the selected elements.
Charts can be defined in the Chart Settings frame (Figure 75). Click on the
buttonin the Dialog toolbar to bring up the Chart Settings frame. A table at the left side of
the frame displays all entities in the active canvas. The Chart Tree at the right side
displays all defined charts for the model in the active canvas. Select one or moreelements on the canvas, right-click on tree root-element and select Create New Chart
item to create a chart that tracks the element value over simulation time. Alternatively,
right-click the selected elements on the canvas and choose Create Chart commandfrom the canvas context menu. A newly created chart is added to the tree. To add anentity/process/connector to an existing chart, select the element on the canvas,
highlight the chart name in the tree, right-click on it and select the Add Selection from
Canvas item. You can also delete charts; Highlight names of charts to be deleted in
the Chart Tree and choose Remove Selected from the context menu.

Figure 75

6.2.5 Run Simulation
Once the setup described in the previous section is completed, you can startand control the simulation using the VCR-like controls in the Simulation toolbar. The
toolbar allows you to startand pausethe simulation. Also, you can step throughthe simulation with the Step Play button. The buttons
andinvoke simulation
with animated tokens moving between entities. The simulation can be stopped at any
time with the Stop and Initialize button. These simulation commands are alsoavailable in the Simulation menu in the Menu Bar.
During the simulation, active components in the model are highlighted in red.
You can also see the values of the entities updated during the run. Charts defined in
the Chart Settings frame are displayed and updated with the frequency specified by
the Plot Update Interval option in the Simulation Settings frame.

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6.3

Viewing and Analyzing Simulation Results

6.3.1 Simulation Charts
Entity values in charts are color coded. The Entity List on the right side of thewindow enables you to change the visualization and color of each single entity plot.
Right click on a chart to open a pop up menu with options to customize the chart(Figure 76).

Figure 76Properties... — opens a dialog box, in which you can set the chartvisualization details.

Save as... — Turns on and off the tool tip for the graph. If the option isenabled, you can see the value of the data point once you move themouse cursor over it.

Zoom In/Zoom Out — Turns on and off the zoom in feature. You candrag the mouse cursor over an area in the chart to zoom into it.

Auto Range — adjusts the range of x and y axis.



Rasterize - freeze a specified chart for later analysis or comparison,

Copy To Clipboard - Copy the time series to system clipboard in a tabseparated text format. You can paste the chart data to anotherapplication, e.g., Microsoft Word or Excel

6.3.2 Simulation History Frame
For your model, you can run simulation many times, changing the simulation
parameters or making corrections to the model itself. The Simulation History frame
allows you to view and manage all the simulation log files (CSV and CIL) that havebeen saved for the given model file.
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If the simulation results are logged (see Simulation Log), each simulation run
is saved into a separate CSV file along with the model representation at the time thesimulation was done. A log file with the extension .csv is created in the folder [projectname].output, where [project name] is the name of the Cell Illustrator model file. For
example, logs for the Cell Illustrator apoptosis.csml sample project are kept in theapoptosis.output subfolder.
All saved simulation logs are accessible in the Simulation History frame.

Figure 77

This frame allows you for viewing and comparing of several simulation runs.
You can also manage the list of logs by opening the context menu with the following
commands: Open Log, Open Directory, Export CIL/CSV File, Open Backup CSML.
With the Rerun command you can open the selected log files for replay in the Cell
Illustrator Player application.

6.4

Running SECG Simulation

SECG (Simulation Engine Code Generator) is a new mode of runningsimulations in Cell Illustrator. SECG is based on the same HFPNe model as thestandard simulation engine described above, however it is faster, more customizable
and adds some viewing and running modes. The basic concept in SECG consists ingenerating a computer program which execution will perform the simulation. Also the
SECG module allows exporting the source code of this program; this allows running
the simulation from other program, and enables controlling the simulation behavior
and data in a more precise way.
The simulation can be run using SECG, by selecting Use SECG option in
Simulation Settings frame. The SECG simulation is performed in the Cell Illustrator
workspace in the standard, interactive way using the Step Play, Play, Pause, Fast
Play, etc. buttons; the results of the simulation are displayed on charts and/or saved tolog file.
Note: The javac compiler that is included in the JDK is required to run SECG
simulations. The JDK (Java Development Kit) must be installed on your computer;
JRE is not enough to run SECG.

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6.4.1 Passing Parameter simulation
The SECG module offers a method for performing a series of simulations (in abulk) on the same model with varying the selected parameter value(s). This mode
allows comparing the simulation results when initial parameters differ. It allows
checking, for example, how the amount of control entity influences the system orchoosing the right value of some kinetic constant. The changing parameters are
selected and the range of their values is defined in the SECG Passing Parameter
dialog box. After that for each combination of parameter values a simulation is run.
This mode is faster than running each simulation as a different SECG simulation and
it is run outside the Cell Illustrator.

Figure 78

The results of Passing Parameter simulation are presented as 2D or 3D charts.
After running Passing Parameter simulation a control panel window appears, whichallows from comparing simulation results by selecting the chart type, and parameters
values. The charts always present how the entities depend on one selected entity while
the rest of the parameter values is fixed.

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6.4.2 Reference Simulation
This simulation mode can be run by invoking Simulation | Run Reference
Simulation. Reference Simulation gives the user the ability to:-insert reference data contained in CSV file into the simulation
run.

-visually compare simulation results versus reference data.

Figure 79

To setup a reference simulation:-

Load the data file. The file should be a tab-separated values filewith time series data stored in columns. The first row must havethe column headers (names). The first column must be the timecolumn, while the next columns should be identified by unique
names.

-

Map the column names from data file to entity names of thesimulated model-

Choose the method and moment of setting values from the list ofavailable choices: Only At Simulation Start, When Presenting
Data, At Each Step.

-

Press the Run button. If the Reference Simulation setup wascorrect, the simulation will be started in the CI workspace.

-

Use the standard buttons Step Play, Play, Pause, Fast Play, etc.
to continue the simulation in the usual way; the results of thesimulation are displayed on charts and/or saved to log file.

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6.4.3 Advanced SECG topics
The SECG program is composed of two components, one holds common
functionality the second contains information specific for the simulated model. The
SECG allows the change and extension of the common module - see the EngineBase
options in Simulation | Setup SECG menu. This enables the usage additional
functions/scripts, or performing operations which would normally would be
impossible in the Cell Illustrator, for example saving and using previous values of theentities.
The source code of the engine generated by SECG can be exported to file inJava or other languages. The source code file can then be edited, which allows forbetter control over the simulation. Also the simulation source code can be integrated
or executed in another program, which is useful for performing parameter
optimization, or generating some advanced statistics from the engine run.
The exact reference for advanced usage, which contains description how the
SECG framework works and detailed information of the content of each source code
file is obtainable from Masao Nagasaki (masao@ims.u-tokyo.ac.jp or check
http://www.csml.org).
Note: Profiting from the SECG engine possibilities to the full extent requires some
software development skills.

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7 Tools for Model Analysis

7.1

Navigator

For a large model, the Navigator frame (Figure 80) provides a convenient way
to locate and display in the canvas window a desired part of the model. The Navigator
displays a small view of the entire model, with a green rectangle marking the portionof the model currently shown in the active canvas. You can drag this rectangle aroundin order to navigate the model in the canvas window.

Figure 80© 2002-2017 Human Genome Center, Institute of Medical
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7.2

FinderWith the Finder frame, you can search for specified elements in the active
canvas (Figure 81). As with the Navigator, the Finder becomes very useful when themodel in the canvas is large or very complicated.

Figure 81© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

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7.3

Print and Print Area

In CI, you can output the whole canvas, a part of active canvas, or thesimulation charts to a printer or an image file in the Postscript, PNG or JPEG format.
This functionality is available via Print and Print Area options in the File menu.

7.4

Export Options

Analysis of model structure and simulation results can be also carried out
outside Cell Illustrator, with third-party tools. The options Export |Entity List, Export |Process List and Export | Connector List in the File menu store respective data in afile in the CSV (Comma Separated Values) format. Such a file can be later uploaded
to a spreadsheet, for example.

Figure 82© 2002-2017 Human Genome Center, Institute of Medical
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8 Making Complex Models
This chapter presents features of CI that are especially useful for large-scalemodeling.
8.1

Grouping and Ungrouping

When editing a model, it is often convenient to treat a group of elements in thecanvas as one element. This can be accomplished with the Group command. This
command is accessible in three ways: via the popup menu in the canvas, Element |Group in the main menu and the Group icon in the Edit toolbar. Once elements aregrouped you can select and move them around easily just as one element. The
grouped elements can be ungrouped with the Ungroup operation. Again, there arethree ways to ungroup elements: via the popup menu in the canvas, Element |Ungroup in the main menu and the Ungroup icon in the Edit toolbar.

Figure 83© 2002-2017 Human Genome Center, Institute of Medical
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© 2002-2017 Human Genome Center, Institute of Medical
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9 Cell Illustrator Player
Once a CI log file is created during a simulation, it can be opened in CI Player
application for replaying and detailed analysis.

Figure 84. CI Player Workspace and Charts.

The main window of CI Player very much resembles the Workspace window
in Cell Illustrator. However, instead of the Simulation menu and toolbar of Cell
Illustrator, CI Player has the Replay menu and toolbar with commands for moving
forward and backward over the time series in the log. The model state (entity values
and process status) at a given simulation time point is visualized in the CI Player
canvas window. On the other hand, charts in CI Player show entity value changes© 2002-2017 Human Genome Center, Institute of Medical
Science, The University of Tokyo. All rights reserved.

Page 72 of 85

over the whole simulation period, with a vertical bar indicating the time point
visualized in the canvas.

Figure 85. Process state indication in CI Player, from left to right: firing, waiting and notfiring

One advantage of replaying the simulation from a log as opposed to rerunning it in
Cell Illustrator is the capability to deterministically repeat the simulation even for amodel of stochastic nature. Also, you can quickly navigate to a specific part ofsimulation and step through it forwards and backwards for debugging purposes. For
more details, please refer to the CI Player online help and CI Player Reference
Manual. You can open them from the CI Player Help menu.

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Appendix A:
Tutorial—Modeling Fas Ligand Induced Apoptosis

Apoptosis Induced by Fas Ligand
Apoptosis, programmed cell death, is known to participate in various
biological processes such as development, maintenance of tissue homeostasis andelimination of cancer cells. Malfunctions of apoptosis have been implicated in many
forms of human diseases such as neurodegenerative diseases, AIDS and ischemic
stroke. Reportedly, apoptosis is caused by various inducers such as chemicalcompounds, proteins or removal of NGF. The biochemical pathways of apoptosis arecomplex and depend on both the cells and the inducers.
Fas-induced apoptosis has been studied in detail and its mechanism has been
proposed as shown in Figure 1 [4]. We will represent this mechanism as a model with
Cell Illustrator.

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Figure 1: Proposed steps of apoptosis induced by Fas ligand: Fas ligands,
which usually exist as trimmers, bind and activate their receptors by inducing receptor
trimerization. Activated receptors recruit adaptor molecules such as Fas-associating
protein with death domain (FADD), which recruit procaspase 8 to the receptor
complex, where it undergoes autocatalytic activation. Activated caspase 8 activates
caspase 3 through two pathways; the complex one occurs when caspase 8 cleaves Bcl2 interacting protein (Bid) and its COOH-terminal part translocates to mitochondria
where it triggers cytochrome c release. The released cytochrome c binds to apoplectic
protease activating factor-1 (Apaf-1) together with dATP and procaspase 9 andactivates caspase 9. The caspase 9 cleaves procaspase 3 and activates caspase 3. The
other pathway occurs when caspase 8 cleaves procaspase3 directly and activates it.
The caspase 3 cleaves DNA fragmentation factor (DFF) 45 in a heterodimeric factor
of DFF40 and DFF45. Cleaved DFF45 dissociates from DFF40, inducing
oligomerization of DFF40 that has DNase activity. The active DFF40 oligomer causes
the internucleosomal DNA fragmentation, which is an apoptotic hallmark indicativeof chromatin condensation. (Note, this is not a CI model.)The pathways consist of several steps < where two different pathways from
caspase 8 are assumed> and many molecules including Fas receptors, the caspasefamily which includes aspartic acid-dependent cysteine proteases and products of
their zymogens; the Bcl-2 family which includes pro- and anti-apoptotic proteins,
cytochrome c and DNA fragmentation factor. The apoptosis starts from the Fas ligand
binding to Fas receptors and ends in the fragmentation of genomic DNA, which isused as a hallmark of apoptosis. Thus the amount of DNA fragmentation can beassumed to be proportional to the cell death.
We have designed a model through utilization of the known facts about theFas-induced apoptosis pathways shown in Figure 1 and biochemical knowledge about
reactions. Figure 2 shows the model representation that we have described with Cell
Illustrator.

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Figure 2: A CI model representing the Fas-induced apoptosis obtained from
Figure 1: For Bid (m11), Procaspase-9 (m21), Procaspase-3 (m25), DFF (m30), DNA(m37), the initial concentration of each compound is assumed to be 100. On the other
hand, for FADD (m4), Procaspase-8 (m5), Apaf-1 (m17), dATP/ADP (m18), when
two compounds react together without the stimulation of apoptosis, the initialconcentrations and the rate are assumed to be 39.039 and m1*m2/5000, respectively
to keep the stable state condition. Each compound is assumed to be produced by therate of 0.5 (represented by a process without any incoming connector) and to degrade© 2002-2017 Human Genome Center, Institute of Medical
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by the rate of its concentration divided by 200 (represented by a process without any
outgoing connector), which keeps its concentration at 100 under the stable state
condition. This degradation rate also applies to other compounds in the network. The
rate of other processes is determined roughly by following Table 1. Synthesis andcatabolism processes are added in the model for all proteins. Autocatalytic processes
are also added in the model to all caspases since they exist as proenzymes. The
pathway from caspase 8 to caspase 3 is assumed when the caspase 8 concentration isover 30. Protease is often synthesized as a proenzyme (zymogen) and changed toactive form by other enzymes or by itself. So the autocatalytic process is added toevery caspase reaction.

Table 1: Functions assigned to continuous processes in the simulation of
apoptosis induced by Fas ligand, where mA and mB represent the contents of thecorresponding continuous entities.
Rate

Unimolecular reaction

Bimolecular reaction

Self-effacement

mA/200

Oligomer

mA/20

mA*mB/10000

MonomermA/10

mA*mB/5000

Enzyme binding

mA/5

mA*mB/2500

Enzyme reactionmA*10

By using the apoptosis scheme modeled as a model, we simulated the amountof DNA fragmentation by varying the Fas ligand concentration and Figure 3 shows
the simulated relationship. It shows that under very weak stimulation (very low
amount of Fas ligand), DNA fragmentation does not occur since the stimulation stops
at the intermediate point because of the assumption of degradation processes. With
the increase of the stimulation, the reaction proceeds to the backward intermediates
and DNA fragmentation (cell death) occurs finally, which increases with the increaseof the Fas ligand concentration.

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Figure 3: Simulated relationship between the amount of DNA fragmentationand the Fas ligand concentration: At higher concentration of Fas ligand, the direct
pathway from caspase 8 to caspase 3 contributes to the fragmentation. To examine theeffect of autocatalytic process of caspases, DNA fragmentation is simulated for bothcases of the presence and absence in this process.

There are two pathways from activated caspase 8 to caspase 3, one throughseveral steps including the cytochrome c release from mitochondria when theconcentration of activated caspase 8 is low, and the direct one to caspase 3 when theconcentration of activated caspase 8 is high [3]. We assume arbitrarily that the direct
pathway starts when the concentration of activated caspase 8 is larger than 30.
Reportedly the removal of the Bid by gene knockout method increases the resistanceof liver cell apoptosis by Fas ligand, while it does not affect the apoptosis of thymus
and embryonic cells. If the second pathway is included in the scheme, DNA
fragmentation increases slightly, especially, when the Fas ligand concentration is high(Figure 3). However the detailed mechanisms of the selections of these two pathways
from caspase 8 are still unclear and need to be studied in future.
Since the presence of autocatalytic process is proposed in caspases [2], it isincluded in our model (Figure 4), which increases the DNA fragmentation as shownin Figure 3.

Figure 4: A model representation of autocatalytic process in Figure 2.

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However, if the large rate of the autocatalytic process is assumed in thecaspase reaction, the DNA fragmentation becomes independent of the Fas ligand
concentration, which then disagrees with the experimental results. Therefore, we canguess that autocatalytic processes must be slow if they are present. To examine theeffect of autocatalytic processes of caspases on the apoptosis by Fas ligand, DNA
fragmentation is simulated when the stimulation by Fas ligand is stopped after a shortperiod. Table 2 shows a simulation result where the apoptosis proceeds more with theincrease of the autocatalytic rate of caspases even for a short period stimulation.
Table 2: DNA fragmentation at four autocatalytic rates of caspases (rate0=0,
rate1=mA*mB/80000, rate2=mA*mB/40000, and rate3=mA*mB/25000), which areassigned to the process TA in Figure 4. The stop time represents the period after thatFas ligand stimulation is stopped. The initial Fas ligand concentration is set to be n =600. Variables mA and mB represent the contents of the continuous entities going intoTA.
DNA Fragmentation
Stop time

rate0

rate1

rate2

rate3

10

0

0

0

1169

15

251

442

746

1862

20

417

581

885

2048

Figure 5 shows simulated time courses of the model in Figure 2 with CI. Someintermediates during apoptosis at three levels of Fas ligand concentrations aremeasured. These time courses might be useful to plan new experiments such asaddition of inhibitors to various steps. However, it is necessary to estimate therealistic rates of each reaction by comparison with the experimental data. It is also
necessary to add other pathways through the Bcl-2 family [1] or p53 to describe thereal apoptosis held in various cells and by various inducers. CI is a very useful tool
for biochemists to describe the complex biological pathways semi-quantitatively on afigure.

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Figure 5: Simulated time courses of some intermediates during apoptosis for
the Fas ligand concentration n=210, 450, and 600.
(References)[1]Harada, H. (1999) Regulation of apoptosis by BH3 domain only proteins.
Jikkenigaku, 17, 1603-1606.
[2]Hugunin, M., Quintal, L.J., Mankovich, J.A. and Ghayur, T. (1996)
Protease activity of in vitro transcribed and translated Caenorhabditis elegans cell
death gene (ced-3) product. Journal of Biological Chemistry, 271, 3517-3522.
[3]Kuwana, T., Smith, J.J., Muzio, M., Dixit, V., Newmeyer, D.D., andKornbluth, S. (1998) Apoptosis induction by caspase-8 is amplified through themitochondrial release of cytochrome c. Journal of Biological Chemistry, 273, 1658916594.
[4]Nijhawan, D., Honarpour, N. and Wang, X. (2000) Apoptosis in neuraldevelopment and disease. Annual Reviews of Neuroscience, 23, 73-87.

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Appendix B: Using Differential Equations in CI
In this example, the following differential equations are modeled with Cell Illustrator.
1 t
1 t
m1(t )  m1(0)   m1(t )dt m 2(t )   m1(t )dt
10 0
10 0
,
, m1(0)  10 , m 2(0)  0

Figure 1 Differential equation results.

Figure 2
Figure 1 shows the differential equation results. For the model in Figure 2, thesimulation results for runs with Sampling Interval equal to 0.1[pt], 5[pt] and 10[pt]are shown in Figure 3, Figure 4, and Figure 5, respectively. Comparing simulation
results to those in Figure 1, you can easily notice that the smaller the samplinginterval becomes, the more similar (accurate) the simulation result is. Note that Cell
Illustrator offers modeling features that are alternative to modeling with differential
equations and allow the simulation of complicated systems in an easier way; (i)discrete behavior and continuous behavior can be mixed in one model in Cell
Illustrator, (ii) the notion of delay can apply to discrete processes, (iii) activity andthreshold concepts can be used in CI.

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Figure 3 Sampling interval is 0.1[pt].

Figure 4 Sampling interval is 5[pt].

Figure 5 Sampling interval is 10[pt].
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Appendix C: Kinetic Styles
This chapter gives more details on the kinetic style concept. The table below
specifies the mathematical formula that is used to calculate the entity value change(delta) for all the various kinetic styles. Also, the parameters used for each kinetic
style are listed in the table.

Kinetic style

Process type

Customcontinuous(speed calc
style)

delta = speed * sampling interval

discrete(add calc
style)

delta = add

continuous(speed calc
style)

delta = connector_speed *sampling_interval

discrete(add calc
style)

delta = connector_add

continuous(speed calc
style)

delta = m_product * coefficient1 *coefficient2^[number_of_input_entities] * stoichiometry *sampling_interval

parameter:
speed / add

connector
customparameters:
speed / add /update for
eachconnector

Massparameters:
coefficient1,
coefficient2,
stoichiometryfor eachconnector

Formula for the change value delta

where m_product is the product of all input entity
values. For the input entity variables (m1, m2, ..., mn)
m_product = m1 * m2 * ... * mn

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discrete(add calc
style)

delta = m_product * coefficient1 *coefficient2^[number_of_input_entities] * stoichiometry
where m_product is the product of all input entity
values. For the input entity variables (m1, m2, ..., mn)
m_product = m1 * m2 * ... * mn

stochastic
masscontinuous(speed calc
style)

parameters:
coefficient1,
coefficient2,
standard
deviation andstoichiometryfor eachconnectordelta is randomly generated using the Gaussian
distribution with the calculated delta_mean andspecified standard deviation.
delta_mean is calculated using the formula for themass kinetic style:delta_mean = delta_mass(coefficient1, coefficient2,
stoichiometry)

discrete(add calc
style)

delta is randomly generated using the Gaussian
distribution with the calculated delta_mean andspecified standard deviationdelta_mean is calculated using the formula for themass kinetic style:delta_mean = delta_mass(coefficient1, coefficient2,
stoichiometry)

connector
rate

continuous(speed calc
style)

parameters:
rate andstoichiometry
discretefor each(add calc
connector
style)

delta = rate * stoichiometry *sampling_intervaldelta = rate * stoichiometry© 2002-2017 Human Genome Center, Institute of Medical
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Page 84 of 85

CI References
1. Doi A, Fujita S, Matsuno H, Nagasaki M, Miyano S (2004) Constructing biological pathway
models with hybrid functional Petri nets. In Silico Biology, 4(3):271-291.
2. Doi A, Nagasaki M, Fujita S, Matsuno H, Miyano S (2004) Genomic Object Net: II. Modelingbiopathways by hybrid functional Petri net with extension. Applied Bioinformatics 2:185–188
3. Matsuno H, Doi A, Hirata Y, Miyano S (2001) XML documentation of biopathways and theirsimulations in Genomic Object Net. Genome Informatics 12:54–62
4. Matsuno H, Doi A, Nagasaki M, Miyano S (2000) Hybrid Petri net representation of gene
regulatory network. Pacific Symposium on Biocomputing 5: 341–352
5. Matsuno H, Fujita S, Doi A, Nagasaki M, Miyano S (2003) Towards biopathway modeling andsimulation. In: 24th International Conference on Applications and Theory of Petri Nets (ICATPN
2003). Volume 2679., Lecture Notes in Computer Science, pp 3–22
6. Matsuno H, Murakami R, Yamane R, Yamasaki N, Fujita S, Yoshimori H, Miyano S (2003)
Boundary formation by notch signaling in Drosophila multicellular systems: experimental
observations and gene network modeling by Genomic Object Net. Pacific Symposium on
Biocomputing 8:152–163
7. Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S (2003) Biopathways
representation and simulation on hybrid functional Petri net. In Silico Biol. 3:389–404,
http://www.bioinfo.de/isb/toc_vol_03.html/.
8. Nagasaki M, (2004) A Platform for Biopathway Modeling/Simulation and Recreating Biopathway
Databases
Towards
Simulation,
Ph.D
Thesis,The
UniversityofTokyo,
http://genomicobject.net/pub/nagasaki_phd.pdf.
9. Nagasaki M, Doi A, Matsuno H, Miyano S, (2003) Recreating biopathway databases towards
simulation. In: Computational Methods in Systems Biology. Volume 2602 of Lecture Notes in
Computer Science., Springer-Verlag, pp 191–192
10. Nagasaki M, Doi A, Matsuno H, Miyano S, (2004) A versatile Petri net based architecture formodeling and simulation of complex biological processes. Genome Informatics 15(1): 180-197.
11. Nagasaki M, Doi A, Matsuno H, Miyano S, (2004) Genomic Object Net:I. A platform formodeling and simulating biopathways. Applied Bioinformatics 2:181–184
12. Nagasaki M, Doi A, Matsuno H, Miyano S, (2004) Integrating biopathway databases for largescale modeling and simulation. In: The Second Asia-Pacific Bioinformatics Conference. Volume
29 of Conferences in Research and Practice in Information Technology., Australian Computer
Society, pp 43–52
13. Nagasaki M, Doi A, Matsuno H, Miyano S, (2005) Bioinformatics Technologies, Chen, Yi-Ping
Phoebe Ed, Springer Press, 179-243, ISBN 3540208739.
(Note) Genomic Object Net is the research version of Cell Illustrator. When Cell Illustrator is cited,
please cite the literature [11].

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