USING MULTI-AGENT SYSTEMS TO STUDY PARACRINIENNE
CELLS INTERACTION
Lynda Dib
University Badji Mokhtar, Computer Science Department, BP12 Annaba, Algeria
Keywords: Multi-agent model, System of multi-agent simulation, Agent interaction, Paracrinien communication.
Abstract: This paper presents our multi-agent framework for modelling and predicting the emergent behaviour
resulting from the presence of distinct environmental conditions that lead to bad interaction of cells in their
tissue. As the cellular interaction is an important behaviour permitting the survive of cells in their tissue, the
objective of our simulator is to be a virtual world of cellular biology while analyzing and simulating the
control mechanisms during the paracrinien communication between cells in order to help its specialists to
better understand, to good interpret and to warn changes of cell states according to its actual internal state
and to the state of its environment.
1 INTRODUCTION
Biological, especially the study of the human body is
a complex field. In biological phenomena numerous
parameters intervene but their exact influence is
often difficult to determine. If it is easy to find a
mathematical model describing the evolution of an
illness for example, but it is difficult in contrast to
model and to understand what happens at the cell
level.
The modelling of the biological and medical
systems by the multi-agent approach is in its early
stages (Giuliano, Denzinger, Merelli, Miles,
Tianfield, &Unland, 2005). Several recent works are
interested in the modelling of cellular behaviour, for
example, the modelling of intracellular signals
(Boss, Jonker, & Treur, 2005). Other works are
interested on the intercellular modelling, for
example, the multi-agent simulation of cellular
migration (Dib, Guessoum, Bonnet, & Laskri, 2005)
(Dib, Guessoum, Laskri, Fartas, & Guettar, 2006)
(Dib, Guessoum, & Laskri, 2006). The multi-agent
system models the neurons functioning (Colloc,
2005), and the control mechanisms modelization, for
the formation of granulomes during the tuberculosis
infection (Segovia-Juarez, Ganguli, & Kirshner,
2004). Using multi-agent systems to study cells
interaction (Dib, &Guessoum, 2007) (Dib, 2008).
Our system falls into in this category. The aim of our
work is about offering to biologists the possibility to
model and to simulate the complex systems
containing cells, molecules and their interactions in
their environment. Agent paradigm provides a very
good solution to model and simulate cells and their
interactions, especially the paracrinien
communication.
Paracrinien signaling, acting on cells
immediately adjacent to the sending cell (to a
proximity <1mm), is a complex phenomenon.
Paracrinien signals (mediators) are chemical
substances with local diffusion, in an extracellular
middle (Berridge, 1985). They are recognized by
every cell using the membrane’s receptors (Smith,
Hill, Lekowitz, Handler, & White, 1983).
That the cell is considered as a structural and
functional unit of the organization cannot live in
isolation, so it is necessary that there is a highly
developed communication network between it
(Masliah, & Housset, 2008). The big vital functions
(such as the breathing, digestion, movements, etc)
are not possible because cells communicate
between each other in a harmonious way. Every
cell receives and sends signals permanently toward
the neighbouring cells (Berridge, 1985) (Smith,
1997). These multiple signals are messages that cells
interpret. In response to the whole received message,
the cell chooses an action: to divide, to specialize or
to die (Chauffert, 2004).
This article presents our system simulating the
paracrinien communication. It is organized as
follows: In the second section, we describe the
proposed model for the simulator realization. The
third section will be devoted to the simulation and
276
Dib L. (2009).
USING MULTI-AGENT SYSTEMS TO STUDY PARACRINIENNE CELLS INTERACTION .
In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 276-280
DOI: 10.5220/0001555702760280
Copyright
c
 SciTePress

the results obtained by the system. The conclusion is
made in section fourth.
2 MULTI-AGENT MODEL
Our system is composed of three categories of
agents: Cells, Molecules and Mediators. We also
distinguish a set of objects: the environment, the
receptors and the extracellular matrix.
2.1 The Agents
2.1.1 The Cells
The AgentCell is principally defined by a set of
characteristics represented by the following
parameters:
– CE: Cellular Energy,
– NR: Number of Receptors bounded to the cell,
– LinkR: state of the liaison between the cellular
membrane plasmic and the Receptors. This last,
takes the value 1 if the receptors are attached to
the AgentCell membrane plasmic, the value 0 if
this junctions is not established,
– NRBM: Number of Receptors Bounded to the
Mediators,
– NFR: Number of Free Receptors,
– NAR: Number of Active Receptors,
– NNAR: Number of No Active Receptors,
– StatC: internal Stat of AgentCell,
– VN: Vector of AgentCell Neighbouring,
– VR: Vector of Receptors belonging to the
AgentCell.
In a paracrinien interaction the communicating
AgentCell realizes the following actions:
– definition of the achieved goal (secretion or
reception of a paracrinien signals) according to
the internal state of the signalling AgentCell and
to the environmental information;
– sending (secretion) of the mediator by the
signalling AgentCell;
– activation of the target AgentCell’s receptor;
– reception of the mediator by the target
AgentCell;
– interpretation of the mediator (signal captured by
the target AgentCell’s receptor);
– response appropriate to the received mediator.
From the biological real characteristics of the
communicating cell the automaton concerning its
behaviour is realized (Figure 1).
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Survival targets AgentCell
Proliferated targets AgentCell
differential targets AgentCell
Died targets AgentCell
Targets AgentCell
Figure 1: Behaviour Automaton of a communicate cell.
2.1.2 The Molecules
The cells consist of a molecule assembly. All the
activities of the cell, including the different cellular
structure formations, depend on the interaction of a
particular group of molecules. We distinguish three
important groups: water, the inorganic ions and the
organic molecules.
The AgentMol is principally defined by a set of
characteristics represented by the following
parameters:
– numMol: number of the Molecule,
– RC: Rate of Concentration of the actual quantity
of molecules in the environment. The RC may be
increased ‘RCA’ or decreased ‘RCD’.
– ‘LimitMin’, ‘LimitMax’, express a minimal and
maximal limit of resource or molecule
concentration, in the environment, that will not
be clear.
2.1.3 The Chemicals Mediators (CM)
A Chemical Mediator (CM) is a molecule that can
be attached to a cellular receptor. In our system a
CM is generated from a molecule.
An AgentCM has the same characteristics as an
AgentMol. It is described in our model by a set of
characteristics represented by the following
parameters:
– State: describes the current state of the
AgentCM,
– LCMR: Link between Chemical Mediator and
Receptor. This parameter takes the value 1 when
the link between AgentCM and receptor is
established and 0 in the contrary case,
– CMInterpreted: this parameter takes the value 1
when the AgentCM is interpreted by the target
AgentCell and 0 in the contrary case,
– CMA: CM Active or no.
USING MULTI-AGENT SYSTEMS TO STUDY PARACRINIENNE CELLS INTERACTION
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Also, an AgentCM possesses certain behaviour
during the communication between two AgentCells.
At every behaviour the AgentCM passes from its
current state to another that can be the inactivate
state, the destroyed or the captured state.
From the biological real characteristics of the
chemical molecule (mediator) the automaton
concerning its behaviour is realized (Figure 2).
Secretes the chemical
messenger
Ties to one
AgentCell targets
Targets AgentCell
interpreter the messenge
r
Targets AgentCell
answers by an
apoptose
Targets AgentCell answers
by a proliferation or a
differentiation or a survival
Signalling AgentCell
AgentCM
Created & Activated
AgentCM
Interpreted
AgentMC
Inactived
AgentCM
Captured
AgentCM
Destroyed
Figure 2: Behaviour Automaton of a communicate
mediator.
2.1.4 The Objects
In addition to these agents, we find other very
important entities in the simulated system: the
environment and the receptors.
– The environment is represented by a particular
class ‘Environment’, and evolves dynamically to
every change of the cellular and the mediator
state. It contains a cell population, represented
by a vector ‘Vcell’, a population of molecules
and resources for the cellular survival (sugar,
k+...) represented by another vector ‘Vmol’,
– The receptors are protein molecules situated on
the membrane or in the cell. The receptor is
principally defined by a set of characteristics
represented by the following parameters:
 TR: Type of Receptor,
 SR: Stat of Receptor. This parameter takes the
value 1 where the junction between CM and
receptor is established and the value 0 in the
contrary case,
 RA: Receptors are activated or not. This
parameter takes the value 1 where the
Receptor is activated, the value 0 in the
contrary case.
3 SIMULATION
Our model has been implemented in the DIMA
environment (Development and Implementation of
Multi-Agent system) (Guessoum, Meurisse, & Briot,
2002). DIMA groups classes that can be re-used
and/or adapted to easily construct agents.
The simulator, witch is the scheduler can be
activated, suspended, resumed or stopped.
In our multi-agent simulator the paracrinienne
communication is realized by an interaction between
the AgentCells and an interaction between the
AgentCell and AgentMC.
Figure 3: Simulation of the communication.
After a time T, the communicating AgentCell
executes the following signalling stages:
– If the AgentCell is in the sending state (it is a
signalling AgentCell), so it secretes (activates) a
messenger in the extracellular environment. This
messenger is an AgentCM sent from the
signalling AgentCell to its nearer AgentCell,
(Figure 3 (a&b)).
– To identify the neighbour AgentCell that has the
smallest distance, target AgentCell, we applied
the equation used in (Dib, 2008).
– Once the target AgentCell and AgentCM are
identified the communication, the attraction
between specific receptors of targets AgentCell
and AgentCM, will be achieved as follows:
 the AgentCM looks for identifying a specific
receptor to this mediator in its membrane. In
an affirmative case, it sends an attraction
a)- Send of the mediator at the instant t0.
Target
AgentCell
Secreted
Mediator
Signalling
AgentCell
b)- Send of the mediator at the instant t1.
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signal to the AgentCM but in the contrary case
it ignores it.
 When the research is positive, the AgentCM-
Receptor link will be established, and the
AgentCM passes from its active state to the
captured state (Figure 4). In the contrary case
the AgentCM will seek among other
AgentCell’s neighbors the opportunity to
communicate. If no neighbor has a specific
receptor permitting the establishing of the
AgentCM-Receptor link so this AgentCM will
be ignored by any neighbor and it will passes
from its current status (active) to the new one
"destroyed".
Figure 4: Simulation of the attraction of the chemical
mediator by the target cell.
 Once the attraction between target AgentCell
and AgentCM is realized then the target
AgentCell responds to this action by treating
the received signal and generating an
appropriate response;
 Once the cellular response is generated so the
target AgentCell passes to a new state
corresponding to the produced response
(Figure 5);
 The AgentCell finish the communication by the
inactivation of the AgentCM (mediator). This
Figure 5: Simulation of the communication: Target
AgentCell responses to this message by a proliferation and
the system generate a daughter AgentCell.
stage must be fast to permit other mediators to
express themselves. An AgentCell can react
simultaneously to origin signals. The
evolution of the simulation is illustrated by the
following figures.
Figure 6: Simulation of a new communication: Send of the
mediator.
Figure 7: Simulation of a new communication: Target
AgentCell responses to this message by a proliferation and
the system generate a daughter AgentCell.
Figure 8: Simulation of communication between two pairs
of AgentCell.
Cellular Answer Generated by the Signalling
AgentCell.
The link of the AgentCM to the target
AgentCell’s receptor provokes an appropriate
cellular response that can be the cellular survival, the
cellular proliferation, the cellular differentiation or
the cellular death (Figure 9).
Attraction of the mediator by
the target AgentCell’s receptor
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279

AgentCell signalant
Signaling AgentCell
AgentMC
AgentCM
AgentCell cible
Target AgentCell
Proliferation
Proliferation
Differentiation
Differentiation
Apoptose
Apoptosis
survival
survival
I nformat i ve Molecul e
Cel l
Cellular answer
Figure 9: Interaction paracrinien: communication between
two AgentCells and target AgentCell’s response after its
interaction with the AgentCM
In our framework, this response, which is the
result of the interaction between two different types
of agent (AgentCell and AgentCM), moves the
AgentCell and the AgentCM from their current state
to another state appropriate for the generated
response.
4 CONCLUSIONS
In this article, we presented our system that we have
realized under the multi-agent platform DIMA. It
allows to model and to simulate the biological
cellular environment specifically the cellular
interaction process via chemical mediators
(paracrinien communication). This modelling is
achieved through interactions between the different
agents of the system.
The simulation achieved by our system reflects
the reality of the biologic nature. The objective of
our system is to be a virtual world of this cellular
biology helping its specialists to better understand,
to good to interpret and warn changes of cell states
according to its actual internal state and to the state
of its environment.
In this system, the cells (AgentCell)
communicate with each other to live, to control their
growth as well as for regulating their functions. The
cell is either normal (in an initial state), or signalling
cell (secrets mediator) and whether a target cell
(receipts mediator and generates an appropriate
response). At the same time, the chemical mediator
(AgentCM) is either active (identified and attracted
by the target AgentCell’s receptor) and whether an
ignored mediator (not identified by the target
AgentCell).
From our simulator, all these biologic
phenomenons are studied and simulated as well as
the evolution of a cellular population in the time is
calculated and is presented to the user by a sequence
of animated images.
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