Medicina
An Agent Oriented Software Engineering Approach for the
Adult Stem-Cell Modeling, Simulation and Visualization
Projetos para o Semestre
Maíra Gatti
04/09/2007
Agenda
• Medicina
– Motivation
– The Research Hypothesis and the MAS adequacy
– Related Work and the MAS advantages
– An Agent-based Approach to Model and Simulate Stem-Cell
• The Stem-Cell Self-Organization Description
• The Cell Life Cycle Scope
• The Agent-based Model
• The Agent-based Simulation
– Discussion
• COOPN
– Conclusions and Future Works ....
• …. Self-Organizing MAS Software Engineering
– Related Work
• Self-Organizing MAS and Autonomic Computing
• Projetos para o Semestre
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Motivation
• How do stem cells behave in the human body?
• How to predict about how and why stem cells behave either
individually or collectively?
• Formal models (for instance)
– ordinary differential equations (Lei & Mackey, 2007)
– cellular automata (Agur et al, 2002)
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Motivation
• Medicine point of view
– The model serves as a reference, a guide for interpreting
experimental results
– The models serves as a powerful means of suggesting new
hypotheses
– The simulation lets us test experimentally unfeasible scenarios
and can potentially reduce experimental costs
• Software engineering
– How can we model such a large and complex system?
– How to minimize the complexity of the design and
implementation of simulation ?
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Motivation
• Agent-Oriented Software Engineering (AOSE)
– clean design in the modeling phase
• Current Drawback
– Model’s semantics
– Model and program reuse is limited
• Such dynamic structures can be intuitively represented and
efficiently implemented in agent-oriented simulators.
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The Research Hypothesis and the MAS adequacy
LANDIC - UFRJ
Aneuploidy => Cancer
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The Research Hypothesis and the MAS adequacy
• Stem cells are (Loeffler & Roeder, 2002 ):
– a potentially heterogeneous population of functionally
undifferentiated cells,
– composed of multi-cellular organisms.
• capable of:
– homing to an appropriate growth heterogeneous environment;
– proliferation;
– production of a large number of differentiated progeny;
– self-renewing or self-maintaining their population;
– regenerating the functional tissue after injury with
– flexibility and reversibility in the use of these options.
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The Research Hypothesis and the MAS adequacy
• Basic agent’s concepts and capabilities (Jennings, 2000)
– Autonomy
– Interactivity
– Adaptation
– Suitable abstractions
– Flexibility for modeling globally emergent behavior
– Distribution and heterogeneity
– Open systems
– Self-organization
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The Research Hypothesis and the MAS adequacy
• MAS is an effective way to understand how stem cells
organize themselves, and to deal with it emergent global
behavior
• The agent-based simulation
– suggests how tiny changes in individual stem cell behavior
might lead to disease at the global through the emergent
behavior,
– allows temporal analysis,
– reduce costs and risks, and
– could avoid some ethical issues.
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Related Work and the MAS advantages
• Existing approaches -> recast in the agent-based modeling
and simulation framework
– it has demonstrated a number of clear advantages of the agent
approach over existing approaches
– (d’Inverno & Saunders, 2005)
• Mathematical models (Agur et al, 2002, Lei & Mackey, 2007)
– don’t allow expressing partial information about a system, i.e.
to formally describe open systems
– explosion of differential equations to model it
– absence of an abstraction for the models
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Related Work and the MAS advantages
• Monte Carlo methods (Metropolis & Ulam, 1949)
– probabilistic dependent
– as learning and adaptation are not possible to implement
• Agent-based formal models
– (Theise & d’Inverno, d’Inverno & Prophet, d’Inverno &
Saunders, 2003, 2004, 2005)
– the stem cell behavior modeled was
• too simple,
• feasible and
• not adequate or evolvable to an adequate model for the physicians’
experiments
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An Agent-based Approach to Model and Simulate
Stem-Cell
• The Stem-Cell Self-Organization Description
• The Cell Life Cycle Scope
• The Agent-based Model
• The Agent-based Simulation
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The Stem-Cell Self-Organization Description
• The niche
– a specialized cellular environment
– provides stem cells with the support needed for self-renewal
– contains the cells and proteins that constitute the extra cellular
environment
• The niche has regulatory mechanisms:
– It saves stem cells from depletion
– It protects the host from over-exuberant stem-cell proliferation
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The Stem-Cell Self-Organization Description
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The Cell Conceptual Model
• The most actives components during the cell life-cycle
• The mitosis division
– which is the stem cell division during the self renew process
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The Cell Conceptual Model
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The Cell Life Cycle Scope
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The Agent-based Model: MAS-ML Modeling
• Structural Elements
– Environment
– Main Organization: Niche
– Organizations: Cell, StemCell, ProgenitorCell, DifferentiatedCell,
Cytoplasm
– Objects: CellMembrane, NuclearMembrane, Nuclei, Chromosome,
Chromatid, Centrosome, Centriole, Substances, Protein,
Organelles, Centromere, Kinetochore, Microtubule, Astral
Microtubule, Polar Microtubule, Kinetochore Microtubule
• Static Diagram
– Organization Diagram
– Role Diagram
– Class Diagram
• Dynamic Diagram
– Sequence Diagram
• Agent interactions
• Goals, plans and actions
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The Agent-based Model: MAS-ML Modeling
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The Agent-based Model: MAS-ML Modeling
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The Agent-based Model: MAS-ML Modeling
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The Agent-based Simulation
• Developed with Java
• We developed a small framework with
– a set of functionalities as send and receive messages,
– the instantiation of the environment, and
– the hierarch structure between agent, role, organization, goal, plan
and action
• Visualization tool goals
– offer to the users a visual environment by means of which is
possible to follow the Macro and Micro levels of the simulation of
stem cell’s cellular life cycle in the niche;
– perceive the difficulties of implementation of the proposed model;
– validate initially the model, verifying if the simulated behaviour in
the prototype has similarities with the behavior of the real entities
(stem cell’s); and
– compare the semantics of a tool build based on MAS approach to
this domain with tools build in other approaches.
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The Agent-based Simulation
• The cells and the images associated to them in each phase
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The Agent-based Simulation
• The cells and the images associated to them in each phase
Macro level
Internal Process
Interface
Micro level
Cell Data Interface
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Discussion
• COOPN (Concurrent Object-Oriented Petri Nets)
– (Biberstein et al, 2001)
– object-oriented specification language based on synchronized
algebraic Petri nets
– defines Petri nets and coordination between Petri nets using
object-oriented approach
– Obs: with Petri Nets it was unfeasible
• Comparisons
– Much easier and fast than building the agent-based solution
– However,
• it was not flexible and suitable for the physicians needs
– For instance, adjusting parameters and observe emergent behavior
• Or, not feasible -> explosion of subnets
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Discussion
• Our agent-based model solution overloaded the CPU even
with few numbers of entities
• the agent-based model solution was not developed
considering the visualization process, and the process
control
– Hence it was a hard task to incorporate the interface to the
framework.
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Conclusions
• The stem cell researchers’ collaborators were very excited
with the first results
• First emergent phenomenon observed
– the differentiated cells are located at the colony’s extremity
while the specialized and stem cells are located at the colony’s
centre
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Future Works
• A review and a set of tests on the proposed model
• Produce reports about Macro and Micro levels to support the
comprehension of the information visualized during the
simulation process
• Increase scalability and performance
• 3D visualization
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Future Works
• Elicit and design the underlined optimized self-organization
mechanism
– When does occur a symmetric/ asymmetric division?
• Activators /inhibitors
• Differentiation and self-renew process optimization
• Potentiality influence
• Probabilistic influence
– Self-organization evaluation
• Differentiation and self-renew rate optimum
• Learning (?)
– Observable behavior vs. Simulation
• How to identify local rules in a way that the perturbation induce a cancer?
• How to identify a cancer in the emergent behavior?
– HOWEVER...
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Self-Organizing MAS Software Engineering
• How to elicit adaptation’s requirements?
• How to design adaptation mechanisms?
• How to measure adaptation features?
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Self-Organizing MAS Software Engineering
• Fundamental engineering issue:
– How to design the local behaviour of the agents to get a certain
required global result?
• Current Agent-Oriented Methodologies:
– Focus on engineering microscopic issues
• e.g. the agents, their rules, action-selection, knowledge
representation, how they interact, protocols, …
– No explicit support for engineering macroscopic behaviour
• A fundamental problem is the lack of a step plan that allows
to systematically
– specify desirable macroscopic properties,
– map them to the behaviour of individual agents,
– build the system, and
– verify it to guarantee the required macroscopic properties, i.e.
a full life-cycle engineering process or methodology.
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Related Work
• Engineering Self-Organizing Systems - Luca Gardelli
– Meta-model
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Related Work
• Engineering Self-Organizing Systems - Luca Gardelli
– Methodologial apporach
• Modelling:look for the desired global behaviour among the
catalogue of known self-organizing systems and model the
strategy
• Simulation: preview the dynamics of the abstract models to see if
emergent properties actually happens
– Models are expressed in a formal language able to describe stochastic
phenomena
• Tuning: Tune the model and related parameters to obtain the
specific required behaviour and evaluate feasibility
parameters
Model
Simulate
Tune
unfeasibility
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Related Work
• Analysing and Engineering Self-Organising Emergent
Application
– Tom De Wolf – PhD Thesis
– A methodology based on the Unified Process
– Customisations for Self-Organising Emergent Systems
• Requirements Analysis
• Design
• Verification
• Iterative Feedback
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Related Work: Tom De Wolf
• A methodology based on the Unified Process
– Iterative Process: 1 iteration
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Related Work: Tom De Wolf
• Designing Self-Organising Emergent Systems based on
Information Flows and Feedback-loops
– Emergence
• Problem-solving power from coordination
– Coordination
= information exchange to execute complementary actions
– Information inherently decentralised
 needs to come to location of agent
– Self-Organisation
• Prerequisite = feedback-loops in information exchange
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Related Work: Tom De Wolf
• Use ‘Information Flow’ as design abstraction:
– stream of information
– from source towards destination localities (= limited
region/part)
– maintained and updated to reflect changes
– aggregate/combine new information into flow along the way
 bring local signal of global situation in destination locality
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Related Work: Tom De Wolf
• Architectural View showing
– Macroscopic Information Flows
– Microscopic Agent Actions
– Relations between them
• = bigger coordination picture
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Related Work: Tom De Wolf
Notation
• Modelling Language: UML 2 Activity Diagrams
– Determines runtime-effects:
• when behaviours start and stop
• Inputs and outputs
• Routing of control and data through a graph
– Serve the need to denote activities in different localities in the
system
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Related Work: Tom De Wolf
• Feedback Loop
Locality 1
Locality 2
Action 1
Locality 3
Feedback Loop
Action 3
Info 2
Info 1
Action 2
Action 4
Action 5
Info 3
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Selg-Organization and Autonomic Computing
-
Self-X properties are typically macroscopic properties in a
decentralised system.
• Focus:
– Decentralised Autonomic Computing
based on Self-Organising Emergent Principles
• Problem:
– No global control, only local control
– Hard to give guarantees about global behaviour
–  not acceptable in industrial context unless …
• Goal:
– Obtain guarantees about overall self-X behaviour
• Formal proof or Numerical Algorithms
– Hard, practically infeasible to obtain equations that describe the
evolution of those requirements
– Therefore, we need measurements that represent these requirements.
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Related Work
• Analysing Self-Organising Emergent Behaviour using
Advanced Numerical Methods
– Tom De Wolf
– “Equation-Free” Numerical Analysis using
• Realistic simulation models
• Existing scientific analysis algorithms
– Feasible compared to formal proof
– Allows advanced scientific results compared to mere
observation of simulations
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Related Work : Tom de Wolf
• Equation = partial-differential equation
–  given a value for the macroscopic variable, gives a new
value that is reached when the system evolves some time step
further
Equation-based Model
Xi
Xi+1
Analysis
Algorithm
Analysis Results
• No need for equation itself, only its evaluation  replace
this with an estimation based on realistic simulations
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Related Work : Tom de Wolf
• Only simulation code is needed, no formal model or
equation
simulate
simulate
…
measure
initialise
…
simulate
Xi
Xi+1
Analysis
Algorithm
Analysis Results
I. G. Kevrekidis, et al. “Equation-free, coarse grained multiscale computation: enabling microscopic simulators
to perform system-level analysis.” Communications in Mathematical Sciences, 1(4):715-762, 2003
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Concluindo...
• Células-tronco são Sistemas Auto-Organizáveis (SAOs)
• Distributed Autonomic Computing também são Sistemas
Auto-Organizáveis
• Sistemas Multi-Agentes Auto-Organizáveis podem ser
utilizados como uma forma de implementação
• Raríssimos trabalhos sobre Metodologias e Processos para
Sistemas Multi-Agentes Auto-Organizáveis
– Somente 2 foram encontrados na literatura
– Ainda com muitas limitações
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Trabalhos para o Semestre
• Re-projetar e implementar o simulador de células-tronco
– Objetivos
•
•
•
•
•
Evoluir especificação
Propriedades globais emergentes
Framework para evoluir processo de diferenciação para vários tipos de células
Visualização 3D da célula
Verificação – Aplicar Equation-free method
– Participantes
• Maíra Gatti, Diego Bispo, Geisa Faustino, Eurico Vasconcellos, Guilherme Pate
• Projetar uma aplicação de AC (implementação poderia ficar a posteriori
de acordo com o tamanho)
– Objetivos
• Projetar propriedades de self-* através dos princípios de auto-organização
– Participantes
• Maíra e Cristiano (?) :)
• Desafios
– Estender AUML com Diagramas de Atividade de UML 2
– Inspiração nos feedback-loops de De Wolf para projetar propriedades
mascrocópicas
– Aplicar Equation-free method na análise dos resultados
– Aumentar escalabilidade e performance -> Distribuição, Grid?
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Trabalhos para o Semestre
• Alunos novos poderiam...
– Auxiliar na modelagem
• Células-Tronco (+ 1 aluno)
• Autonomic Computing (+ 1 aluno)
– Auxiliar na construção do framework de células-tronco
• Diego + 1 aluno que seria o mesmo da fase anterior
– Auxiliar na fase de verificação
• Guilherme + 1 aluno
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References
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Lei, J., Mackey, M.C. (2007). "Stochastic differential delay equation,
moment stability, and appplication to hematopoietic stem cell regulation
system", SIAM J. Appl. Math. (2007), 67(2), 387–407.
Agur, Z., Daniel, Y. and Ginosar, Y. (2002). The universal properties of stem
cells as pinpointed by a simple discrete model. Mathematical Biology,
44:79–86, 2002.
Loeffler, M. and Roeder, I.. Cells Tissues Organs, 171(1):8-26, 2002.
d’Inverno, M. and Saunders, R. (2005). Agent-based modelling of stem cell
organisation in a niche. In Engineering Self-Organising Systems, volume
3464 of LNAI. Springer, 2005.
Theise, N. D. and d’Inverno, M. (2003). Understanding cell lineages as
complex adaptive systems. Blood, Cells, Molecules and Diseases, 32:17–
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d’Inverno, M. and Prophet, J. (2004). Modelling, simulation and
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NETTAB, pages 105–116, 2004.
d’Inverno, M. and Saunders, R. (2005). Agent-based modelling of stem cell
organisation in a niche. In Engineering Self-Organising Systems, volume
3464 of LNAI. Springer, 2005.
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References
• Mirko Viroli,Matteo Casadei, Luca Gardelli A Case of SelfOrganisingEnvironment for MAS: the Collective Sort Problem. 4th
European Workshop on Multi-Agent Systems (EUMAS 2006).
December 2006 -Lisbon -Portugal
• Matteo Casadei, Luca Gardelli, Mirko Viroli. Simulating Emergent
Properties of Coordination in Maude: the Collective Sorting Case.
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Languages and Software Architectures (FOCLASA 2006) a satellite
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• Matteo Casadei, Luca Gardelli, Mirko Viroli. Collective Sorting
TupleSpaces. Daglioggettiagliagenti: Sistemi Grid, P2P e Self-*,
AI*IA/TABOO Joint Workshop (WOA 2006). September 2006,
Catania–Italy
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Environment of Self-OrganisingMulti-Agent Systems Exploiting
Formal Analysis Tools. AICA 2006 - AssociazioneItalianaper
l'Informaticae ilCalcoloAutomatico. September 2006, Cesena-Italy
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(E4MAS 06). To be held at The Fifth International Joint Conference on
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FIM
Maíra Gatti
mgatti@teccomm.les.inf.puc-rio.br